{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "RKThngF7hCtY"
      },
      "outputs": [],
      "source": [
        "#Data Fetching\n",
        "!pip install opencv-contrib-python\n",
        "from IPython.display import clear_output\n",
        "!unzip \"/content/Total Images.zip\" -d \"/content/\"\n",
        "!unzip \"/content/Total GT.zip\" -d \"/content/\"\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "AhzI1ZLchcd8"
      },
      "outputs": [],
      "source": [
        "#Useful imports\n",
        "#will remain the same\n",
        "from tensorflow.keras.layers.experimental.preprocessing import StringLookup\n",
        "from tensorflow import keras\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2 as cv\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6k5QaeNxhqT_",
        "outputId": "68cf2c2c-e167-4233-bd3e-7f890b803486"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5160\n",
            "5160\n"
          ]
        }
      ],
      "source": [
        "filenames_img=sorted(os.listdir('/content/Total Images'))\n",
        "filenames_gt=sorted(os.listdir('/content/Total GT'))\n",
        "filenames_imgsplit=[filename.replace('.jpg', '') for filename in filenames_img]\n",
        "filenames_gtsplit=[filename.replace('.txt', '') for filename in filenames_gt]\n",
        "print(len(filenames_imgsplit))\n",
        "print(len(filenames_gtsplit))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRDA2kaYhzX4",
        "outputId": "f7b56ead-e3c2-44f2-c733-08c56a5d1612"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total training samples: 4128\n",
            "Total validation samples: 516\n",
            "Total test samples: 516\n",
            " Example from training dataset AHTD3A0003_Para1_1_word1\n"
          ]
        }
      ],
      "source": [
        "#train test validate splitting\n",
        "split_idx = int(0.8 * len(filenames_imgsplit))\n",
        "train_samples = filenames_img[:split_idx]\n",
        "train_samples_split = filenames_imgsplit[:split_idx]\n",
        "test_samples = filenames_img[split_idx:]\n",
        "test_samples_split = filenames_imgsplit[split_idx:]\n",
        "\n",
        "val_split_idx = int(0.5 * len(test_samples))\n",
        "validation_samples = test_samples[:val_split_idx]\n",
        "validation_samples_split = test_samples_split[:val_split_idx]\n",
        "test_samples = test_samples[val_split_idx:]\n",
        "test_samples_split = test_samples_split[val_split_idx:]\n",
        "\n",
        "assert len(filenames_imgsplit) == len(train_samples) + len(validation_samples) + len(\n",
        "    test_samples\n",
        ")\n",
        "\n",
        "print(f\"Total training samples: {len(train_samples)}\")\n",
        "print(f\"Total validation samples: {len(validation_samples)}\")\n",
        "print(f\"Total test samples: {len(test_samples)}\")\n",
        "print(f' Example from training dataset {train_samples_split[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "15RdGQJuilt6"
      },
      "outputs": [],
      "source": [
        "#get data and labels as lists\n",
        "base_path='/content/'\n",
        "base_image_path = os.path.join(base_path, \"Total Images/\")\n",
        "base_GT_path = os.path.join(base_path, \"Total GT/\")\n",
        "\n",
        "def get_image_paths_and_labels(filenames_img, filenames_imgsplit):\n",
        "    paths = []\n",
        "    labels = []\n",
        "    for i in range(len(filenames_imgsplit)):\n",
        "        img_path = os.path.join(\n",
        "            base_image_path,  filenames_img[i]\n",
        "        )\n",
        "        if os.path.getsize(img_path):\n",
        "            paths.append(img_path)\n",
        "            label_path = os.path.join(\n",
        "            base_GT_path,  filenames_imgsplit[i]+'.txt'\n",
        "        )\n",
        "        label_file = open(label_path, \"r\") \n",
        "        labels.append(label_file.read())\n",
        "\n",
        "    return paths, labels\n",
        "\n",
        "train_img_paths, train_labels = get_image_paths_and_labels(train_samples, train_samples_split)\n",
        "validation_img_paths, validation_labels = get_image_paths_and_labels(validation_samples, validation_samples_split)\n",
        "test_img_paths, test_labels = get_image_paths_and_labels(test_samples, test_samples_split)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjrxJp1lqIaX",
        "outputId": "d3557e5b-1354-4292-d609-aba806158f2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum length:  7\n",
            "Vocab size:  39\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['أت', 'ظافر', 'يق', 'را', 'ي', 'لؤ', 'بن', 'رؤوف', 'بصحبة', 'م']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Find maximum length and the size of the vocabulary in the training data.\n",
        "train_labels_cleaned = []\n",
        "characters = set()\n",
        "max_len = 0\n",
        "\n",
        "for label in train_labels:\n",
        "    label = label.split(\" \")[-1].strip()\n",
        "    for char in label:\n",
        "        characters.add(char)\n",
        "\n",
        "    max_len = max(max_len, len(label))\n",
        "    train_labels_cleaned.append(label)\n",
        "\n",
        "print(\"Maximum length: \", max_len)\n",
        "print(\"Vocab size: \", len(characters))\n",
        "\n",
        "# Check some label samples.\n",
        "train_labels_cleaned[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "gBSqAB2tqeoR"
      },
      "outputs": [],
      "source": [
        "def clean_labels(labels):\n",
        "    cleaned_labels = []\n",
        "    for label in labels:\n",
        "        label = label.split(\" \")[-1].strip()\n",
        "        cleaned_labels.append(label)\n",
        "    return cleaned_labels\n",
        "\n",
        "\n",
        "validation_labels_cleaned = clean_labels(validation_labels)\n",
        "test_labels_cleaned = clean_labels(test_labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoUQ7b9TT-Q-",
        "outputId": "06d56481-1fdc-430c-d59e-996e4133583f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ي', 'ه', 'ك', 'ل', 'ة', 'ا', 'ز', 'ب', 'م', 'خ', 'و', 'ط', 'ح', 'ء', 'ث', 'ؤ', '،', 'ش', 'آ', 'ق', 'ف', 'ض', 'ص', 'ت', 'ج', 'إ', 'أ', ':', '.', 'ن', 'د', 'ع', 'ٍ', 'ر', 'ئ', 'ذ', 'ظ', 'س', 'غ'}\n"
          ]
        }
      ],
      "source": [
        "print(characters)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Oy1gxSJMqonS"
      },
      "outputs": [],
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "# Mapping characters to integers.\n",
        "char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)\n",
        "\n",
        "# Mapping integers back to original characters.\n",
        "num_to_char = StringLookup(\n",
        "    vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "QPIYYdhCqxfz"
      },
      "outputs": [],
      "source": [
        "def distortion_free_resize(image, img_size):\n",
        "    w, h = img_size\n",
        "    image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)\n",
        "\n",
        "    # Check tha amount of padding needed to be done.\n",
        "    pad_height = h - tf.shape(image)[0]\n",
        "    pad_width = w - tf.shape(image)[1]\n",
        "\n",
        "    # Only necessary if you want to do same amount of padding on both sides.\n",
        "    if pad_height % 2 != 0:\n",
        "        height = pad_height // 2\n",
        "        pad_height_top = height + 1\n",
        "        pad_height_bottom = height\n",
        "    else:\n",
        "        pad_height_top = pad_height_bottom = pad_height // 2\n",
        "\n",
        "    if pad_width % 2 != 0:\n",
        "        width = pad_width // 2\n",
        "        pad_width_left = width + 1\n",
        "        pad_width_right = width\n",
        "    else:\n",
        "        pad_width_left = pad_width_right = pad_width // 2\n",
        "\n",
        "    image = tf.pad(\n",
        "        image,\n",
        "        paddings=[\n",
        "            [pad_height_top, pad_height_bottom],\n",
        "            [pad_width_left, pad_width_right],\n",
        "            [0, 0],\n",
        "        ],\n",
        "    )\n",
        "\n",
        "    image = tf.transpose(image, perm=[1, 0, 2])\n",
        "    image = tf.image.flip_left_right(image)\n",
        "    return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yu3ZTSjyuaZD",
        "outputId": "30c7b2ce-571c-4931-8846-ab2bbb586d57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['أت', 'ظافر', 'يق', 'را', 'ي', 'لؤ', 'بن', 'رؤوف', 'بصحبة', 'م', 'ضرغا', 'بد', 'مظفر', 'نوح', 'ذهب', '.', 'للحج', 'عفيف', 'ن', 'خاز', 'وهلال', 'عطعوط', 'وهو', 'و', 'طفنا', 'لنا', 'وصو', 'عند', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'حاج', 'في', 'جاري', 'كان', '.', 'شيخ', 'مع', 'سعينا', '.', 'رايق', 'ي', 'لؤ', 'بن', '.', 'رؤوف', 'بصحبة', 'م', 'ضرغا', 'مظفر', 'للحج', 'نوح', 'هب', 'ذ', 'عفيف', 'خازن', 'ل', 'وهلا', 'عطعوط', 'فر', 'ظا', 'الخيمة', 'وصولنا', 'عند', '.', 'يلبي', 'آخر', 'إ', 'حاج', 'لحجيج', 'ا', 'قوافل', 'في', 'أت', 'بد', 'جاري', 'كان', '.', 'شيخ', 'مع', 'وسعينا', 'طفنا', '.', 'دراق', '،', 'مشمش', ':', 'لنص', 'ا', 'ا', 'لهذ', 'نسر', '،', 'بث', '،', 'ناء', '،', 'غيظ', '،', '.', 'نسر', '،', 'بث', '،', 'ء', 'نا', '،', 'غيظ', 'ط', 'و', 'ؤ', 'ر', '.', '.', 'بصحبة', 'م', 'غا', 'ضر', 'عطعو', 'مظفر', 'ح', 'نو', 'هب', 'ذ', 'ظافر', 'يق', 'ا', 'ر', 'ء', 'لؤ', 'بن', 'ت', 'أ', 'بد', '.', 'للحج', 'عفيف', 'ن', 'خاز', 'ل', 'هلا', 'إ', 'ج', 'حا', 'لحجيج', 'أ', 'فل', 'ا', 'قو', 'سعينا', 'و', 'طفنا', 'لنا', 'صو', 'و', 'ر', 'جا', 'ن', 'كا', '.', 'شيخ', 'مع', 'لتالية', 'ا', 'لكلمات', 'ا', 'ة', 'ئد', 'فا', 'تعلم', 'هل', '.', 'ر', 'لحج', 'ا', 'د', '،', 'مشمش', ':', 'النص', 'ا', 'لهذ', '.', '،', 'راق', 'د', '،', 'مشمش', ':', 'لنص', 'ا', 'ا', 'لهذ', 'ا', '،', 'بث', '،', 'ء', 'نا', '،', 'غيظ', 'ظافر', 'بصحبة', 'م', 'ضرغا', 'مظفر', 'نوح', 'هب', 'ذ', 'يق', 'ا', 'ر', 'لؤي', 'بن', 'ف', 'ؤو', 'ر', 'ج', 'للحج', 'عفيف', 'ن', 'ز', 'خا', 'ل', 'وهلا', 'ط', 'عطعو', 'حا', 'لحجيج', 'ا', 'قوافل', 'ا', 'بد', '.', 'ي', 'لنا', 'وصو', 'عند', '.', 'يلي', 'أخر', 'ثر', 'إ', 'جا', 'كان', '.', 'شيخ', 'مع', 'وسعينا', 'طفنا', 'ت', 'ا', 'في', 'ننا', 'إ', 'ه', 'غ', 'ص', '،', 'ش', 'ب', '،', 'ض', 'خ', 'ث', 'ا', 'ة', 'فا', 'تعلم', 'هل', '.', 'ي', 'ضرغا', 'مظفر', 'ح', 'نو', 'هب', 'ذ', 'لؤ', 'بن', 'ف', 'و', 'ؤ', 'ر', 'بصحبة', 'م', '.', 'ط', 'عطعو', 'فر', 'ظا', 'يق', 'ا', 'ر', 'للحج', 'عفيف', 'ن', 'ز', 'خا', 'ل', 'هلا', 'و', '.', 'فل', 'قوا', 'أت', 'يلي', 'آخر', 'ثر', 'إ', 'ج', 'لحجيج', 'ا', 'لخيمة', 'مع', 'وسعينا', 'طفنا', 'لنا', 'صو', 'و', 'ا', 'في', 'ي', 'جار', 'ن', 'كا', '.', 'شيخ', 'نا', 'هو', 'و', 'يتكلم', 'بفلس', 'ا', 'مثل', 'فهمها', 'لا', 'بكلمات', 'ه', 'خ', '،', 'ك', 'ع', 'ظ', 'غ', 'ص', '،', 'ش', 'س', 'ب', '،', 'ض', 'التالية', 'هل', 'ا', 'في', 'ا', 'ت', 'لكلما', 'ا', 'ت', 'ئد', 'فا', 'تعلم', 'ن', 'را', 'ي', 'لؤ', 'بن', 'وف', 'ؤ', 'ر', 'بصحبة', 'غام', 'ضر', 'ز', 'مظفر', 'ح', 'نو', 'هب', 'ذ', 'خا', 'وهلال', 'ط', 'عطعو', 'فر', 'ظا', 'يق', 'سعينا', 'آ', 'ثر', 'إٍ', 'ج', 'حا', 'لحجيج', 'ا', 'فل', 'و', 'قو', 'ت', 'أ', 'بد', '.', 'للحج', 'عفيف', 'لنا', 'وصو', 'عند', '.', 'يلبي', 'خر', 'بفلس', 'هو', 'و', 'يتكلم', 'الخيمة', 'في', 'ي', 'جار', 'ن', 'كا', '.', 'نقض', 'لشيخ', 'ا', 'مع', 'ا', 'مثل', 'أفهمها', 'لا', 'ت', 'بكلما', 'ك', 'ع', 'ظ', 'بنا', 'أ', 'بلغ', 'هل', 'را', 'ش', 'لت', 'سأ', '.', 'متك', 'لز', 'بط', 'لضا', 'ا', 'له', 'س', 'ب', 'ض', 'خ', 'ث', '.', 'را', 'لؤي', 'بن', 'رؤوف', 'بة', 'بصح', 'م', 'نوح', 'هب', 'للحج', 'ذ', 'عفيف', 'خازن', 'ل', 'هلا', 'ظا', 'يق', 'لخيمة', 'عند', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'حاج', 'الحجيج', 'افل', 'قو', 'بدا', 'ري', 'جا', 'كان', '.', 'شيخ', 'مع', 'وسعينا', 'له', 'بفلس', 'نقض', 'ا', 'مثل', 'ا', 'لا', 'ت', 'بكلما', 'ئم', 'نا', 'هو', 'و', 'هل', 'سألت', '.متك', ':', 'عفيف', 'را', 'لؤي', 'بن', 'ف', 'رؤو', 'بصحبة', 'ضرغام', 'هب', 'زن', 'ذ', 'خا', 'ل', 'هلا', 'و', 'ظا', 'يق', '.', 'آخر', 'إ', 'حاج', 'لحجيج', 'ا', 'فل', 'ا', 'قو', 'ت', 'ا', 'شيخ', 'بد', '.', 'للحج', 'مع', 'سعينا', 'طفنا', 'وصولنا', 'عند', '.', 'يلبي', '،', 'النص', 'ا', 'لهذ', 'لية', 'التا', 'ت', 'ة', 'ئد', 'ناء', 'فا', 'تعلم', 'هل', '.', 'الحج', '،', '،', 'دراق', '،', 'مشمش', ':', '.', '،', 'اق', 'ر', 'د', '،', 'مشمش', ':', 'النص', 'ا', 'لهذ', '،', 'بث', '،', 'ناء', '،', 'غيظ', 'ل', 'لؤ', 'بن', 'ف', 'و', 'ؤ', 'ر', 'بصحبة', 'م', 'ضرغا', 'مظفر', 'هلا', 'نوح', 'هب', 'ذ', 'و', 'عطعوط', 'فر', 'ظا', 'يق', 'را', 'ي', 'وصولنا', 'لحجيج', 'ا', 'فل', 'قوا', 'بدأت', '.', 'للحج', 'عفيف', 'ن', 'ز', 'عند', 'خا', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'ج', 'حا', 'فهمها', 'لخيمة', 'ا', 'في', 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'تعلم', 'هل', '.', 'الحج', 'في', 'مشمش', ':', 'النص', 'ا', 'لهذ', 'التالية', 'الكلمات', 'ة', '.', '،', 'ق', 'ا', 'ر', 'د', 'نسر', '،', 'بث', '،', 'ء', 'نا', '،', 'غيظ', '.', 'نسر', '،', 'ي', 'صو', 'و', 'عند', '.', 'يلبي', 'آخر', 'ثر', 'ا', 'ء', 'جار', 'كان', '.شيخ', 'مع', 'سعينا', 'و', 'طفنا', 'لنا', 'بفلس', 'هو', 'و', 'يتكلم', 'الخيمة', 'في', 'نقض', 'ا', 'مثل', 'أفهمها', 'لا', 'بكلمات', 'ئم', 'نا', 'جح', 'را', 'سألت', '.', 'متك', 'لز', 'بط', 'لضا', 'ا', 'له', '،', 'ك', 'ع', 'ظ', 'أصحابنا', 'هل', 'ة', 'ه', 'غ', 'ص', '،', 'ش', 'س', 'ب', '،', 'ض', 'خ', 'ئد', 'ث', 'فا', 'تعلم', 'هل', '.', 'الحج', 'في', 'أننا', 'بث', 'د', '،', 'مشمش', ':', 'النص', 'ا', 'لهذ', 'التالية', 'ت', 'الكلما', '،', 'ناء', '،', 'غيظ', '،', 'ق', 'ا', 'ر', '،', 'بث', '،', 'ء', 'نا', '،', 'ل', 'م', 'ضرغا', 'نوح', 'هب', 'ذ', 'وهلا', 'عطعوط', 'ظا', 'رايق', 'لؤي', 'بن', 'رؤوف', 'بصحبة', 'وصولنا', 'أ', 'بد', 'للحج', 'عفيف', 'زن', 'خا', 'عند', '.', 'إ', 'حاج', 'ت', 'لهذ', 'لتالية', 'ا', 'لكلمات', 'ا', 'ة', 'ئد', 'راق', 'د', '،', 'مشمش', ':', 'لنص', 'ا', 'ا', 'عفيف', 'يق', 'ا', 'ر', 'ي', 'لؤ', 'بن', 'و', 'ؤ', 'ر', 'ن', 'بصحبة', 'م', 'ح', 'نو', 'هب', 'ذ', 'خاز', 'ل', 'هلا', 'و', 'عطعوط', 'فر', 'ظا', 'عند', '.', 'يلبي', 'خر', 'آ', 'ثر', 'إ', 'ج', 'حا', 'لحجيج', 'شيخ', 'ا', 'فل', 'ا', 'قو', 'ت', 'أ', 'بد', '.', 'للحج', 'مع', 'سعينا', 'و', 'طفنا', 'لنا', 'صو', 'و', 'بط', 'أ', 'لا', 'ت', 'بكلما', 'ئم', 'نا', 'هو', 'و', 'يتكلم', 'لخيمة', 'لضا', 'ا', 'في', 'جاري', 'ن', 'كا', 'له', 'بفلس', 'نقض', 'ا', 'مثل', 'فهمها', 'ه', 'خ', 'ث', 'ك', 'ع', 'ظ', 'بنا', 'صحا', 'بلغ', 'ع', 'هل', 'جح', 'ا', 'ر', 'لت', 'سأ', '.', 'متك', 'لز', 'ض', 'ش', 'س', 'ب', '،', 'ض', 'نسر', 'ناء', '،', '،', 'ق', 'ا', 'ر', 'د', '،', 'مشمش', ':', 'النص', 'لهذا', 'الكلمات', '،', 'بث', '،', 'زن', 'لصحبة', '.', 'م', 'ضرغا', 'نوح', 'هب', 'ذ', 'خا', 'عطعوط', 'فر', 'ظا', 'را', 'ي', 'لؤ', 'رؤوف', '.', 'قوا', 'بدأ', '.للحج', 'عفيف', 'يلبي', 'آخر', 'إ', 'ج', 'حا', 'لحجيج', 'ا', 'فل', 'ت', 'ن', '', 'شيخ', 'مع', 'وسعينا', 'طفنا', 'وصولنا', 'عند', 'بكلما', 'نائم', 'يتكلم', 'الخيمة', 'في', 'ري', 'جا', 'بلغ', 'بط', 'لضا', 'ا', 'بغلس', 'نقض', 'ا', 'مثل', 'أفهمها', 'لا', 'هل', 'جح', 'ا', 'ر', 'لت', 'سأ', '.', 'لزمتك', 'ة', 'أننا', 'ه', 'غ', 'ص', '،', 'ش', 'ب', 'ئد', '،', 'ض', 'ث', '،', 'ك', 'ع', 'بنا', 'أصحا', 'فا', 'تعلم', '.', 'لحج', 'ا', 'في', '،', 'ق', 'را', 'د', '،', 'مشمش', ':', 'لنص', 'ا', 'ا', 'نسر', 'لهذ', 'لية', 'لتا', 'ا', 'ت', 'الكلما', 'ة', 'ئد', 'فا', 'تعلم', '،', 'هل', 'بث', '،', 'ء', 'نا', '،', 'غيظ', '.', 'عطعوط', 'فر', 'ظا', 'يق', 'ا', 'ر', 'ي', 'لؤ', 'بن', 'ف', 'للحج', 'رؤو', 'بصحبة', 'م', 'غا', 'ضر', 'مظفر', 'نوح', 'هب', 'ذ', 'عفيف', 'ن', 'ز', 'خا', 'ل', 'هلا', 'و', '.', 'م', 'ضرغا', 'مظفر', 'ح', 'نو', 'هب', 'ذ', 'عطعوط', 'يق', 'را', 'ي', 'لؤ', 'بن', 'رؤوف', 'بصحبة', 'ئم', 'مع', 'وسعينا', 'طفنا', 'وصولنا', 'عند', 'نا', 'يتكلم', 'الخيمة', 'في', 'ري', 'جا', 'كان.', 'شيخ', '.', 'رؤوف', 'بصحبة', 'ضرغام', 'مظفر', 'ح', 'نو', 'هب', 'ذ', 'للحج', 'عفيف', 'خازن', 'وهلال', 'عطعوط', 'رايق', 'لؤي', 'بن', 'في', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'حاج', 'لحجيج', 'ا', 'فل', 'قوا', 'جاري', 'ت', 'بدأ', 'ن', 'كا', '.', 'شيخ', 'مع', 'طفنا', 'وصولنا', 'هل', 'له', 'بفلس', 'نقض', 'ا', 'مثل', 'فهمها', 'ء', 'ا', 'لا', 'بكلمات', 'جح', 'يتكلم', 'لخيمة', 'ا', 'را', 'سألت', '.', 'لزمتك', 'بط', 'لضا', 'ا', 'الكلمات', '،', 'ش', 'س', 'ب', '،', 'ض', 'خ', 'ث', '،', 'ك', 'فائدة', 'ع', 'ظ', 'صحابنا', 'أ', 'بلغ', 'تعلم', '.', 'الحج', 'في', 'أننا', 'ه', 'غ', '.', 'فر', 'ظا', 'يق', 'را', 'ي', 'لؤ', 'بن', 'رؤوف', 'بصحبة', 'م', 'للحج', 'ضرغام', 'نوح', 'هب', 'ذ', 'عفيف', 'ن', 'خازا', 'ل', 'وهلا', 'ط', 'عصعو', 'في', 'و', 'عند', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'ج', 'حا', 'لحجيج', 'ري', 'ا', 'فل', 'قوا', 'أت', 'بد', 'جا', 'كان', '.', 'شيخ', 'وسعينا', 'طفنا', 'صولنا', '.', 'ا', 'مثل', 'فهمها', 'أ', 'لا', 'بكلمات', 'نائم', 'هو', 'و', 'متك', 'يتكلم', 'لخيمة', 'ا', 'لز', 'بط', 'الظا', 'له', 'بفلس', 'نقض', 'وهلال', 'رؤ', 'بصحبه', 'م', 'ضرغا', 'مظفر', 'نوح', 'هب', 'ذ', 'عطعوط', 'فر', 'ظا', 'رايق', 'لؤي', 'بن', 'ف', 'و', 'فل', 'وهلا', 'عطعوط', 'فر', 'ظا', 'يق', 'را', 'ي', 'لؤ', 'بن', 'ف', 'قوا', 'رؤو', 'بصحبة', 'ضرغام', 'مظفر', 'نوح', 'هب', 'ذ', 'ت', 'بدأ', '.', 'للحج', 'عفيف', 'خازن', 'ل', 'هو', 'ن', 'كا', '.', 'شيخ', 'مع', 'سعينا', 'و', 'طفنا', 'لنا', 'صو', 'و', 'و', 'عند', '.', 'يلبي', 'آخر', 'ثر', 'إ', 'ج', 'حا', 'لحجيج', 'يتكلم', 'ا', 'لخيمة', 'ا', 'في', 'ي', 'ر', 'جا', '،', 'را', 'سألت', '.', 'متك', 'لز', 'الضابط', 'له', 'بغلس', 'مثل', 'فهمها', 'ك']\n"
          ]
        }
      ],
      "source": [
        "print(train_labels_cleaned)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CFRBXqgKq4eN",
        "outputId": "683f7b1b-e011-4e93-fd72-fee1d93e6b7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "أت\n",
            "tf.Tensor([37 10 99 99 99 99 99], shape=(7,), dtype=int64)\n",
            "أت\n"
          ]
        }
      ],
      "source": [
        "batch_size = 64\n",
        "padding_token = 99\n",
        "image_width = 64\n",
        "image_height = 32\n",
        "\n",
        "\n",
        "def preprocess_image(image_path, img_size=(image_width, image_height)):\n",
        "    image = tf.io.read_file(image_path)\n",
        "    image = tf.image.decode_jpeg(image, 0)\n",
        "    image = distortion_free_resize(image, img_size)\n",
        "    image = tf.cast(image, tf.float32) / 255.0\n",
        "    return image\n",
        "\n",
        "\n",
        "def vectorize_label(label):\n",
        "    print(label)\n",
        "    label = char_to_num(tf.strings.unicode_split(label, input_encoding=\"UTF-8\"))\n",
        "    length = tf.shape(label)[0]\n",
        "    pad_amount = max_len - length\n",
        "    label = tf.pad(label, paddings=[[0, pad_amount]], constant_values=padding_token)\n",
        "    return label\n",
        "    \n",
        "label=vectorize_label(train_labels_cleaned[0])\n",
        "print(label)\n",
        "indices = tf.gather(label, tf.where(tf.math.not_equal(label, padding_token)))\n",
        "label = tf.strings.reduce_join(num_to_char(indices))\n",
        "label = label.numpy().decode(\"utf-8\")\n",
        "print(label)\n",
        "\n",
        "def process_images_labels(image_path, label):\n",
        "    image = preprocess_image(image_path)\n",
        "    label = vectorize_label(label)\n",
        "    return {\"image\": image, \"label\": label}\n",
        "\n",
        "\n",
        "def prepare_dataset(image_paths, labels):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels)).map(\n",
        "        process_images_labels, num_parallel_calls=AUTOTUNE\n",
        "    )\n",
        "    return dataset.batch(batch_size).cache().prefetch(AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIBy0L_rvszy",
        "outputId": "f40fcc17-b02d-47ef-c863-99950e8cbf8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensor(\"args_1:0\", shape=(), dtype=string)\n",
            "Tensor(\"args_1:0\", shape=(), dtype=string)\n",
            "Tensor(\"args_1:0\", shape=(), dtype=string)\n"
          ]
        }
      ],
      "source": [
        "train_ds = prepare_dataset(train_img_paths, train_labels_cleaned)\n",
        "validation_ds = prepare_dataset(validation_img_paths, validation_labels_cleaned)\n",
        "test_ds = prepare_dataset(test_img_paths, test_labels_cleaned)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "2PrS10_qv4CQ",
        "outputId": "4190f96e-5ee9-471d-e659-7b0da7cd150b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 16 Axes>"
            ],
            "image/png": 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i82SMIR6P09XVRWtrq1OkLJvN4vP5NH5bwhRMXQP7kF17ANXS0uI8Z1kWfX19HDlyZMFnoz0eD5s3b2bLli3UajWOHz/OmTNnnOBtrjeumbP9IstBPB5n586dtLa2Ui6XyWazlMtlhoaGOHPmDPl8/mOvkclkOHz4MNFolHg8zsaNG4nFYpd97czS6PF4/GNTKwYHB3nppZc4deoU2WyWVCpFPp+nu7v7YwMxkaWopaWF7du309TUREdHB1u2bKGhocFJj/X7/RhjqFQqTjVal8tFKBRi48aN3HbbbWQyGc6ePauVWZFrYFkWQ0NDHDhwgJaWFjo6Oujo6Kh3s2QOFEzNkcvlwu/34/P5WL9+Pd/61re44447gI9WgJ555hnOnz+/4DcQn8/Hfffdx2/91m9RKpV48sknGRkZoVgsUigUrmlfxsUln0WWInuCoL29nW9+85vcfffdTE1NMTAwQDqd5p133mFsbGxOwdTExAQvvPACR44c4aabbuLXf/3XrxpMNTQ00NHRQSwWIxgMXrWvnDx5kj/6oz9yqnJWKhVqtRqZTIZCoTC/H16kjtavX8+3v/1ttm/fjt/vJxQKzVqBsoOnQqHg3BftfnP77bfT2NhIT08PP//5zxVMiVyDWq3GqVOnePbZZ2lubub++++fNWkvS5eCqTmyz6oJBoPEYjHWrFnD5s2bgekOUKvVaG1tdSr5LSSXy+VsdC+VSjQ3NxMMBoHp1MFr3eSuQEqWA8uy8Pv9tLa2sn79eiYnJ/F6vaTTaU6ePInHM7d/vuyVrHK5TDwep1gsOs/ZA8OZ+7Lcbjcej8c5m+pqcrkcuVxu/j+kyBITCARoa2tj7dq1sybeSqUShUKBarVKuVymVqvhdrudX/YRBB0dHWQymUW5F4qsZJZlkcvlGB0ddSbllDK7PCiYmqOGhgbuvvtutm3bxurVq52l10KhwOTkJIVCgVQqtSjVu2q1GiMjIxw5cgSv18uGDRv4xje+wdjYGG+88QbHjh2b03W0IiXLTSqV4q233iKXy9HQ0EB7ezstLS1Eo9E570mqVCqk02kqlQqpVMpJv4tGo2zYsAHLspiYmGB4eNjpa93d3c7AMJlMztqvpcqYspINDAzws5/9jIMHDwIfTb5VKhUqlQqWZTmHzofDYW677Ta2bNlCpVKhtbWVUChEPp8nFArV88cQWbYuvr+4XC4CgQANDQ2USiWKxaIqxS4xCqbmKBaL8aUvfYnHHnvMKWsOkM/nOXfuHFNTUwwPDy/4Pgl7M+/AwADvvfce8Xic7du38/DDD9PT08Po6CjHjx+/5n1TCqpkORgdHeWFF17gvffe4+abb+Yb3/gGHR0dNDY2znllqlKpMD4+jtvtZmxszDm6IB6Ps23bNqLRKN3d3aRSKarVKsPDwxw6dIimpiY8Hg9NTU2XFHMRWal6enr4wQ9+gNfrnfVdv/h7b4whmUzy9/7e36OlpQW/38/q1avx+Xxks1kFUyKfwMz+Zu9JjMViFAoFJ6Vclg4FUx/DLjphl4NtaWlxyilXq1WKxSJTU1NMTEyQyWSuWs1vvizLIp/PMz4+7qQbJpNJstksjY2NRKNRKpWKk4IxU7lcJpPJMDU1RSAQ0A1OlhV7NalarTI+Pk46nSafzztpRvDxZdQty3KKtdgz6zDdt0OhkHPmlD1xUSqVyGazBAKBy06OKJiSlaxUKjE2Njan1xpjGBsbI5VKOfsL7eIUC8ntduP3+3G73ZTLZYrFovqhrEiVSoVyuUy5XJ41njPGONX9NBm+9CiYugqv18u6detYtWoVq1evpqWlBWMM5XLZSe3r7u7mhRdeoL+/n97eXrLZ7IK2wbIsKpUKPT09ACSTSdasWcPNN99MPB7noYceYu3atZw9e5YXX3zReZ393qNHj/Lkk0/S3NzM7bffzj333OM8J7LUlctlp0LewYMH+eEPf0gikeDYsWNkMhncbvesSQJ7D9PlAqqLSzb7/X6ampqoVCqcPXvW2Ttl56wbYygUCs57Z/6aeZ2Zj6lfyY0kk8nw2muvMTY2htfrJRAI4PP56OvrY2BgwHndtR7rcbG1a9dy7733kkwmOXr0KG+++SaTk5Pqb7Ki2Cnnx48fJ5FIcOedd846B1GWLgVTV2EHU3v27CGZTDpVVcrlMmNjY0xOTnLs2DGef/55Tpw44cwoLLRKpUJvby9nz56ltbWVe+65h1qt5gRTn//859m/fz/d3d2XBFPHjh3j9OnThEIhSqUSe/bsWfD2iSwWO5gyxjA6OsrRo0edleFKpeLs20gkEs57Pq4ghH1z8vv9JBIJqtUqkUjECaby+TwTExO43e5LqgXaVc3sdD9jzKxKZxrcyY0km83y5ptvsnfvXqcvGGOoVqtONcuL08rn00fWrFnD448/ztatW3n66af54IMPmJqamvf1RJYiO5ianJwkHo8zOjo6r2yny61cqZ8sLgVTV2EfWtjU1ERjY6NzaFq1WnXOlJmcnCSfzy96RS9786+98dAeyAUCAWdm/nIHkNrvq9VqTq7t5V4nslTZwY+dluf1ep0N8C6Xi1gsRmNjo/Ma+7DdTCZz1dLpF6f52f3CPrQ3GAxSLpexLAuPx0MikaC9vX3WIdjw0Y0rl8sxNTW1KKm+IkuRnRZr70O0Ayefz0csFnMq+tl9K5vNMjU1NefZdrtveTweIpEIsViMRCIx69y5dDqt2XtZMezq0Ben+c2F3V9CoRDRaBRjDJlMxjmiQAHV4lEwdRVer5euri7uuOMOIpEIyWQSgHQ6zeHDh+nu7ub06dPODNn1YO//KJVKs3JogTkFSdpEL8uN/V3t6uriy1/+MmvXrnVKmtsDt0AgAEz3zUwm45wttXfv3isOtKLRKJs2baK9vd0ptV6tVhkcHKRcLpNMJvnMZz4DQCKR4NFHH+Xmm2+eVdHP3otVq9XYv38/zz///Jz3m4isNHZf7ejo4KGHHqKrq8uZ+LAsi7fffpvnn3+eycnJOV3PXu2yjyUJh8Ps2rWL73znO4yPj/P666/z4osvLnh6vchy5Xa7ue2223jooYcIBAK89NJLvPjii3M6k1HmT8HUVXg8Hjo6OtixYwfBYNAJVvL5PCdPnuS9995jZGTkup8zY2+ktweUMLezo+xAyk5nEllO2tvbeeSRR9izZ89lq1JaluUcZD0wMMCZM2fYt2/fFa8XDodZvXo1pVKJZDKJx+PBsizGxsacYhepVArLsojFYtx///3cd999s8qk26vF5XIZv9/PG2+8oWBKbmiWZZFMJvmVX/kVPv3pTztnUNmz7K+99tqcgqmZaYMejwe/308wGGTLli2sXbuWXC5HPp/n9ddfVzAlwkdFKm666SZ+7dd+jVgsxuTkJK+99pqCqUWmYOoqarUa6XSawcFBwuEwsViMUCg0q0wyXP+lU3sQOfOcnbm0YebgU9VgZLlxu914vV48Hs8le5Vsdj8Ih8OsWrWKrVu3OgUl0uk0uVyOvr4+jh07RjAYJB6Pz1rdhekVaXvg5vV6Z33+xZ9l799yuVw0NTWxceNGgsGgk/pbrVbJ5/O6kcmSZu899Pl8lEolcrncNVfk8/l8JJNJIpEIXV1dxGIxpwKf2+2mVqvR1NTEpk2biEajTsq6fQ7c5QIiO5394rZ6vV58Ph9ut1v3MlnxPB4PLS0tbNy4kcnJScrl8iWT+C6Xy0lzt3/ZqeyxWAzLsigUCrMOrZeFo2DqKorFIu+//z7BYJDm5mbuuusutm7dCjBrMHc9/zGfOUvn9XpnVRabS964PdOnG5AsN/YExtX2/Xk8HlwuFy0tLTz++OPs2bOHvr4+fvSjH7F37156enr43ve+R1NTE3v27OGJJ54gmUw6gzKXy0Vzc7NzOHAikbjiZl57oOfz+bAsi927dxONRkmn05w8eZIPP/yQdDrNsWPHOHnypPZSyZLV0NDAjh07aG9vZ2hoiMOHDzMxMXFN12hpaeFrX/sau3fvprm5mU2bNhEKhWZV8rv99tuJx+NkMhkGBwc5f/48U1NTvPPOOxw6dGjWPWzmpOXMx+20P7sAjchK19DQwOc//3m2bdtGT08P3//+9xkZGZn1Gq/X6+z/tTOp3G43ra2t7Ny5k4mJCXp6ehgYGFBm0iJQMHUV5XKZvr4+fD4f7e3tbNmyZVaZZJjbPqWFZK9IeTwePB7PJZvh5/J+BVKyXM3cr3Q5duprQ0MDu3fvZvfu3XzwwQe8+uqrwPQhwK+88grGGGq1Gg899BDt7e2zVmwbGhpob28nmUwSDoevGkzBRytW69atY/369ZTLZfbu3escEjwyMsKpU6cW469DZEEEAgE6OzvZtGkTfr+fkydPXnMwFYlEuP3223nkkUece9TMIwMsy2L9+vWsX7+eSqVCd3c33d3djI6O0tPTwwcffHDJhODlJgrte+DFK8oiK5Xf72fHjh3s2LGDDz74gOeff/6S19jHhNgZFfbkYCwWY/Xq1YRCIcbGxi452kMWhoKpq7Asi2w2y+joKIFA4LovjzY3N9PR0eGkM7jdbqeSkWVZZDIZ+vv7GRsb4+jRo3MqhHG5tAmR5SCTyThl/uPxOO3t7fj9/nld6+KAbGbqrD2r3tzcTGNjI8YYisUiQ0NDzgHCdorSTKFQiIaGBqfiZ3t7O+FwmKmpKVwuF/l8nv7+foaHhz/R34PIQnC5XASDQXw+H83NzaxatYrOzk7S6bRThe9ar2cP4OyUoosLHnk8Hnw+Hy6Xi3A4TFNTEy6Xi66uLm666Sby+TwjIyOz7mWWZTE1NcXRo0ep1Wo0NjbS0dGxYH8PIitBrVajVCrhcrmcCs4AjY2NbNiwgVgsRl9fn8Z/i0TB1FVUKhXOnz9PKpVySqFfL263m1tuuYWvfvWrNDU1EQwGCYVC+Hw+Ojo6nKpjP/rRj5wNvTPPmLqSj5vZF1mqBgYG+NnPfsa+ffvYtWsXDz/88LyDKfgogJoZSPn9fm6++WaeeOIJ4vE4yWQSl8tFKpXi5z//Oe+++64z4JuZs26MobOzk23bttHQ0OBUAfV4PNx1113O3ssnn3ySF154QaWcpe68Xq+Tzrpu3TruvvtuPvWpT+H3+/nFL34xr2va+zYKhQLj4+MUi8VZ95toNEoikcDtdpNMJmloaCCXy+FyudiwYQNDQ0O88MILHDp0CPhoZer06dN8//vfJ5FIcPfdd/PEE084ExciMn2kh31UTyaToVwuY4zhpptuoq2tjaGhIc6dO8f+/fuVcr4IFExdhb0ylc1miUajzkzb9WCMobW1lV27dtHR0UEkEqGhoQGY7jT2GRtHjx7ltddeu6Z2KZCS5SiTyXDy5EmGh4dpbm52DgX9pGYGVPZG361btxKNRp3XFAoFTp8+zf79+8lkMgwMDJBOp2ddY/PmzU4hitWrVzv91tbb28tzzz2nAaAsCW63m2g0SlNTEy0tLXR2drJ27Vq6u7vntTI1sx/Z5xpms9lZwZTf73eyIyKRCJFIxDnXzev1Eo/H2bt37yXXnpiY4PDhwwQCATo6OigWi7P6p8iNzl6Zss+oqtVqGGNIJBIkk0mi0aiTaSELT8HUHBUKBU6cOMGbb75JNpulpaWFO+64g/HxcVpaWkilUk7u97UO8qLRKOvXrycajeL1egkEAni9Xnbt2kU8Hsfn85HJZBgfH6dUKnH+/HlGRkYYGBjg/PnzcwqOLMvi3LlzvP322xhj6O/v1+y4LCulUolUKkWpVOL48eO8+OKLNDc3E4/HaW5uxufzEY/Hicfj17SXwuVysXbtWj73uc+Ry+XYvHmzcy5OqVRyqo1NTk46M3+Xq3QWDoedmf5YLKbN8bKk+f1+1q1bx86dO+no6FjQ4MTj8RAOh/F4PJRKJfL5PLVajWw2y+DgoFNEwuv1Uq1WcbvdNDU1kclk8Pv9l+zraGpqYteuXTQ3N7N161aCweCCtVVkpbFT0QuFgrNNRBaXgqk5mpqa4uWXX6anp4eOjg4+97nPOWUq+/v7yWazvPXWW/z5n/85g4OD13Ttjo4OHnvsMW666SbncOBAIEAsFqO5uRmAc+fOcfz4cVKpFG+99Rbvv/8+uVxuTvsv7PLNBw8edEo0H4EufWEAACAASURBVD9+/JpL34rUk31+lNvtZmRkhP379xMIBNi5cyd79uwhHo+zY8cOIpGIU9Xv49gVj/bs2cPatWupVCo0Nzfj9/upVqtkMhnS6TQjIyMMDg4yMDBAtVqlXC5fcq1EIsGOHTvo6Ohg9erVzg1s5rlUIktFJBLhjjvu4JFHHiEYDJJIJBbs2j6fj6amJud4kVKpRLVaZWxsjL6+Pmq1GuFw2Omr0WiUrq4u5/GLdXV18Y1vfIOtW7fS2NhINBrVZKDIFdirvel0mkgkMq+VZrk2CqbmqFwuMzg46JRjDYfDdHZ2OitH2WyW06dPEwgErnkWwD48dOPGjUSjUVpbW2fNvNlnCoyOjjI6OsqpU6c4cuTInPJeZ5alnZiY4PTp08B02oRuRrKcVKtV5zufzWYZHh7G4/EQiURYu3Yt1WqVbDZLtVqddTD1zA3wl2OnQjQ2NjqP2QfyVioVSqWSk1pbKpWcNKWZ/dzezB+Px0kkEk5p2pmlnavVqvqcLBl2QaNVq1Y559N8EvZ3vVqtYoxxKooVi0Wnr5TLZdLpNNVq1elHfr+feDxOOBwmHA47Z1PN7LPRaJR169axZcuWWfuyNEEhK9nMPmUXeJmLWq3m3L90z7k+FEzNUbVadfZInDx5kmeeeYYPP/wQn89HQ0ODc2P68pe/PGsvxVwkk0k8Hg+Dg4P09PSQTqdnrRpVq1X6+vro7e11NrLP9SYyc0BZKBSccrcLtd9EpB4aGhpoamoiEAiwZcsWtm/f7qQpdXd3U6lUOHfuHKOjo5w9e5be3t5LrlEoFBgeHiYejxMKhYhEIpcNkCzLoqOjgwcffJDW1tbL3pxcLhc7d+6ks7OTxsZGp6R6sVjk8OHDHD16lKGhIU6dOqWbmywJuVyO999/n1AoRFNTEzt27KCtrQ2fz0cikaClpYVisUgmk5nTxF0qleLll18mlUrR2trKnj17aG1tdfZChUIhp59ZlkUgECAQCMxKCYzH49x3331EIpFZ/WTHjh00Nzc7VTGnpqacolDqT7ISlUol3n//fZ566ikSiQS33HILmzZtqnez5AoUTM1RpVJhbGyMiYkJzp8/T3d3N36/n66uLu69917a29vp6OjgvvvuIxQKXdO1x8fHOXbsGGfOnOHEiRO8+uqrjI+PO8/bezfszYWFQuGabiD2BuBMJuOk+dkzgyLLjTGGxsZGtm7dSjweZ/fu3XzmM58hEAhw4sQJ3n//fcbHx3nttdc4cOAAhUKBycnJS66TzWbp7+/H4/HQ2to6a1XZXnmKRCJOKfa2trYrzoYbYwgEArNSDO2B34svvsj3v/99MpkMk5OT6neyJExNTfHiiy9y5MgRNm3aRCwWo62tjUAgQFtbG52dnUxMTFAsFp37zdW+uyMjI/zkJz/h6aef5tOf/jStra1Ov7JXvexZdph9gLx9ZlRraytf/epX+dVf/dVZnxUMBolGo7jdbqampjh16hSTk5MMDQ2pMpmsSMVikZdffpn33nuP9vZ2/vE//sds2LCh3s2SK1AwdQ3s1aJSqUQ2mwWmc8MnJiac2bZYLEYsFrum69opROl0mvHxcQYGBhblLJqLT5IXqQe32+3kcFcqlcvuP7oSO0ix9xTG43Gi0ahzbEClUmF8fNwp0GLvz7icUqlEOp0mlUoRjUYvGSjaqX52++w0pKuxLMt5vb3hfmRkhP7+fmciQ2QpqFarTnAfj8edcxTtlaJoNDorRe/jlMtlRkdHAejs7GRycpJMJuPsXbSrZfp8vkvSlex7k13l73ITkuVy2alim0qlnGIwuqfJSlSr1ZicnGRqasqZDL+WibiZ1TVl8SmY+oRSqRQHDhygp6eHY8eOcfDgQQKBwDVdY2pqip6eHqamphgcHJx1fo3ISmKMYfv27dx1110EAgHee+89fvnLX84p7dQOoAKBAFu3buW+++4jmUwSiUQ4efIk5XKZvXv38s477zh96Wo3n8HBQV555RWampq466676OzsJBAIOHusstks77zzDocOHZr37Hcul2P//v0q9iJLjl1gpVKpMDU15UwCtLS08OCDD7Jz507279/P8PAwuVzumgZyAwMD/MVf/AXvvvuus/LkdrvZsWMHd95556wjAyqVCsPDw4yNjTnHH8zsuxf/nkqlGB4eplAocPz48WuajBFZbuZzLqjP53OO07EnLnW+6OJSMPUJjY+P8+677zpVwTwezzXPBNibBe0UCN0cZKWy9xZ95zvfIZFI8L3vfY9Dhw7NKZjy+/20trYSjUbZsWMHDz74IG1tbXR3d3P48GFSqRRvvvkmr732GoVCgXK5fNWbx7lz55iYmMDv9xMMBnnwwQedksyVSoXJyUn+5m/+hh/+8IeUSqV5/byWZTltEVlKarUamUyGbDbL5OSk8x1va2vjC1/4ArVaDb/fz8svv3zNmRJnz57lqaeewuPxOPdFr9fL17/+dafips3e39jd3c3Q0BDPPfccBw4cmDX4m9mP7UI09irwfPumyHJyLYGQvZd/5sHWCqQWl4KpT6hWqznpESLy8YwxTkWucDhMU1MTHo/HSfWp1Wrk8/lLBkn2OVKNjY00NjY66UC1Wo1UKsXY2BhTU1Pkcrk5DbAqlQrZbJZyuezshbL3J9qnyE9OTjI+Pq5gSFakmd/5VCrFyMiIM6vtdrudM9ympqZmVdP8OHZK38xVKbfb7aQd2ZMM+XyeXC7H2NiY82t8fJyJiQmnbZdLvxW5EVzrsRr2vdXej+hyuZxU9UKhoAyJRaRgSkSuG8uyOHPmDM888wyJRIJQKMTf//t/n1qtRi6XI5/PMzExweuvv87Ro0dn3URaW1v5whe+wIYNG1izZg0NDQ3UajX6+vp4/fXXGRkZobe395pS8uzSs/DR5vje3l5OnDjByMgI586d0+BNVrzBwUH+/M//nF/+8pds2bKFhx56iM7OTrZt28Zv//ZvMzExwdjYGKOjo3MakIXDYVpbW52qlnZQtW3bNiKRCKVSiV/+8pe88cYbZDIZZ5+w/d9XCqRE5PI8Hg+BQACfz+cUU6rVagwMDNDf38/o6ChDQ0PaY7hIFEyJyHVjWRb9/f289NJLxONxvvjFL/LII48QCARIpVKkUin6+/s5e/YsH3744azBVDKZ5N577+W2227D7Xbj9Xopl8sMDAywd+9ehoeHqVQqcw6mZp4BZd9gqtUq586dY9++fYyPj3P+/HndfGTFGxkZ4a//+q9xuVw88MAD7N69m87OTjZt2sS6desol8v09/fT3d09p1XfRCLhHLALH82s2/02m81y4MABfvjDHzI5OelMpMwMoq4USNkrWyLyEY/HQzAYJBAI4Pf7McZQq9UYHh7m6NGjjI6OMjY2pr6zSBRMich1Y6cUTU1N4XK5KJfLzkxaMBikUqkQi8Vob2+nq6vLORstn887AzG7GtjMXHC70td8bhSWZTE1NUVfXx/pdNo5nyqVSuk8NrkhzKxCOTU1xdmzZ0kkEk65f/sQ3lgsNqeUV7vCZiAQoFAokMlkKJfLVKtVJ73WPvPQ7XY75dPtSp32REk2m3WCN7u/2/ukNMkhN4pqtcro6ChnzpxhYGCAYrGI3+/H5/MRjUad/45EIgQCARKJhHPwdS6XY3h4mPHxcRU3W0QKpkTkupqamuLMmTNEIhGGh4cpFotOMGUHVk888QR33HEHvb29/OVf/iUffPABcPlyr3OZzb4Sy7KoVqvs27eP3//93ycQCDA0NMTQ0BDFYpGxsTEN2uSGcurUKf7wD/+Q5uZmtmzZwp133kljYyOhUIh169Y5pc6vxi7qAsxKw52YmGBkZMQJsNra2jDGEIvFaGhoIBQKsX79elpbWxkeHmbfvn2cPXt21rVHR0edAaVdeVNkJbL3PmUyGZ5++mkOHz5MoVBgaGiI9vZ21q5dyxe+8AXWrl3rFHpxu910dXXR0NBAtVrl1KlTvPTSS0xMTDAwMKD72SJRMCUi11UulyOXy5FOp5mcnHQqWfp8PqcoxV133cWnP/1pDhw4wFtvvcWRI0dmBVIXV8z8JPsrarUaJ0+e5NSpU7OuJ3IjGhwcZGhoCGMMd999N8lkklWrVtHV1UVrayt+v/+arjc6Osq+ffvo7e3l7Nmz9PT0UKlU2LBhAxs3biQUCtHZ2UlrayuNjY3cdtttrF+/njNnzlAoFJxzrmb28ZGREUqlklL+ZEWzi0gUCgXeffdd9u7dSyAQoL29nXg8zvr16/niF7/ILbfcctn3ZjIZBgcHOXToEJOTk5p8WEQKpkSkLmq1mvMPfSKRYNWqVbS1tQEfHc7b0NDApk2bSKVSbNy4kWAw6NwM7IHUQt0cdJMRmWb3q6mpKXp7e52qe5lMxjm3Zq7XOXbsGMPDw6RSKfL5vLOvsVAoOAeS+nw+kskk0WiUYDCIy+UiGAzS1tbmHHZtt6lQKNDd3b1YP7rIkuB2u+no6KC1tRW32+3sBfb5fDQ3N9PQ0MC6deucIi82y7IYGxvj/PnzTE5OcvbsWSqVigq6LDIFUyJSF6VSiTfeeINz586RTCb56le/ykMPPeTsifJ6vXR2dvKtb32Lv/W3/haJRIL29naq1aozY6eZNpHF09vby9NPP+2k4YZCIVwu1zX1uVQqxfnz58nn8xSLRWfP1fj4ONVqlUQiwT333MOtt95KKBQiFosB0NjYyD333MOuXbuca9VqNZ5//nnef/99p3y6yEoUCAR44IEHePTRRwkEAsB0oORyufD7/Xi9XhoaGpwJSFu1WmXv3r089dRTDA0N0dfXp+N7rgMFUyJSF3ZZ87Nnz9Lc3MynP/1pqtWqk9Nt76XYtWuXswplV9671gGdiFy7iYkJUqnUrMfm0+8ud5B9LpejWq3i8Xjw+/2sXr161qpXKBSiq6tr1nuq1SqHDx92zqUTWak8Hg8bN27ks5/9LOFw+LKvmXlum82yLM6ePcurr77KwMDAZfueLDwFUyJSN3bqQbFY5MSJE/ziF7+YVa3PPqjXTu+zV6Ly+TxTU1Pk83mOHz8+p3LNIvLJLMQEhmVZTtnmSqVCLpfjww8/5KWXXnIO+r3ae48cOUKhUNB+KVnRKpUKJ0+e5KWXXnKKucxk96OLVatVjh496hxEL9eHudpftjFG/ydkxbEsa8lN1dzofc3r9dLa2kpzczNutxu3243L5aKxsZHt27fT1tY26zT4vr4+Dh8+zOTkpJMfrtPdl56l1tdu9H62lNgTJl6vl5aWFpqamub0vlQqxeDgIMViUYPFC9TPVh63201bW5tT8fJKLu4DlmUxOjrK+fPn53SMgczd1fqZgim54Sy1Gw+or81kjMHj8eByuUgmk+zZs4fOzk7necuy6O7u5u2332ZycrKOLZWPs9T6mvrZynDxhvsbnfrZynS5yrW2mZOLcn1crZ8pzU9ElhQ7nQ+m91X09/dfctigZt1ERANJWemu9h3X93/p0MqU3HCW2iweqK9djl2xLxAI4PHMnvcpl8sUCgVtQl/illpfUz+TlUj9TGTxKc1PZIalduMB9TVZmZZaX1M/k5VI/Uxk8V2tn7muZ0NERERERERWCgVTIiIiIiIi86BgSkREREREZB4UTImIiIiIiMyDgikREREREZF5UDAlIiIiIiIyDwqmRERERERE5kHBlIiIiIiIyDwomBIREREREZkHBVMiIiIiIiLzoGBKRERERERkHhRMiYiIiIiIzIOxLKvebRAREREREVl2tDIlIiIiIiIyDwqmRERERERE5kHBlIiIiIiIyDwomBIREREREZkHBVMiIiIiIiLzoGBKRERERERkHhRMiYiIiIiIzIOCKRERERERkXlQMCUiNzRjzO8ZY/6g3u0QERG5HGNM1Bjzcr3bIZenYEpEREREZOlKA1/7uBeZaT83xvzgOrRJLlAwJSIrkjFmrTHmiDHmO/Vui8hKZYz5d8aYB+rdDpGVyhgTAX4A7DPG/KuPebkPGAf+eNEbJg4FU0uEMea2C7MJfcaYvzDGJOrdJpFlrhP4b8Av690QkRXs/wDeBDDG/IYx5sE6t0dkpXkMuAP4EvC4Meb+q7zWDbwDBK9Du+QCBVNLx78DuoEvAgPAl+vbHJFlbwgYAax6N0RkpbIsa8KyrOKFPxrgnxhjYsaY/8UYY670vgsTh7uvTytFljZjTJMx5syFVaiLvQWEgL1AAmg3xoSNMX924T1/d8ZrG4BfAf6DMeaRxW+5gIKppeQXwOPA14F/YVnW9+vbHJFlLwH8FvBHxpiN9W6MyEphjHEbY04aY9Zc9NQhYBPweeALlmVddiLjwvtagcnFbanIsrERqFmWlbn4CcuyTgJrgN1ACugD/ocLj30b+H+NMasuvPyfAhngd4EfGGP8M691oZDFKWNMw6L9JDcgBVN1YoxpMcaM219oy7L+d+ARoBE4ZIxpqmsDRZa//8J0nvkbwMdV69PqlcjcJYEuoP+ix/1MpxmdAG4zxvxdY8waY0zcGLPeGPOQMeafMz3D/keWZXVf11aLLF3HgaYLK7pR+0FjjMcYsx34H4GXmM5ceouP+tpppvvbzRfe4ge8TN/3LGD9zA+xLGuK6RTAdYv609xgFEzVz1qgaFlW2n7Asqx3gf8IxICWejVMZIXwAwHgZ8CtF25K/+dl0igSQPa6t05k+Spd+N190eP3A+9YlnWI6cpjv8b0atUo03sXf5fp+9sTlmX9zvVpqsjSZ1lWiun0vC8Co8aYtDEmA5SBly88/n8DD15Y8f2PwARwhOlAqnbhUv8caGY6yIrPeByACytVjcDYYv9MNxJzhVV4WWTGGB+wD+gBXmT6y30L8Fmm0/z+df1aJ7L8GWO+APx7YDMwzPTK719alrVmxmsMcBD4d5ZlPVmXhoosQ8aYfcCzwL8G8sCDwA+BX7Usa1892yaynBljvEwHQi5gyrKs/FVeu43pseQ6y7KGZzz+eeDHQItlWdUZj/8e8IhlWbctUvNvSFqZqhPLskrAXcALwBamy1n+GNigQErkk7Ms6+eWZW0F/iXTaUUjQKsx5k4AY8x64E+ZXr3673VrqMjy9FWmK4yNMR1M/Svgm5cLpIwxX7qQ3iciH8OyrLJlWSOWZQ1dKZAyxgSMMQ8DfwP8ezuQMsZEjDHfYLqS7f9qWVbVGBM0xnzGGPMT4B8yvZdYFpBWpursQgn0nwLvA//dsqxX69wkkRXBGNMG/AOmc83vsSzrgDHmd5hOg/AynT7xI+CfWZY1VL+WiixfxhgPELqwF+NKrwkAfsuyVHBC5BO4sJ+qDwgDh4H/YFnWk8aYHUwfURBkevLwX1uW9fSFwOpPgJPAU8AfzlzBkoWhYKrOjDEupvNby0BuRolZEZkHY8xvA/8WqDKdQvu/WZb1wYznXUATMGZZVu3yVxEREVlaLqSmtwDjlmWVZzzu5cJeqIvS+gJAxbKsynVv7A1EwZSIrCjGmDDTs+Dj9W6LiIiIrGwKpkRERERERObBc7UnjTGKtGTFsSzL1LsNF1Nfk5VoqfU19TNZidTPRBbf1fqZqvmJiIiIiIjMg4IpERERERGReVAwJSIiIiIiMg8KpkREREREROZBwZSIiIiIiMg8KJgSERERERGZBwVTIiIiIiIi86BgSkREREREZB4UTImIiIiIiMyDgikREREREZF5UDAlIiIiIiIyDwqmRERERERE5kHBlIiIiIiIyDwomBIREREREZkHBVMiIiIiIiLzoGBKRERERERkHhRMiYiIiIiIzIOCKRERERERkXlQMCUiIiIiIjIPCqZERERERETmQcGUiIiIiIjIPCiYEhERERERmQcFUyIiIiIiIvOgYEpERERERGQeFEyJiIiIiIjMg4IpERERERGRefDUuwEiIiIiIrL4jDH4fD7cbje1Wo1SqUStVqt3s5Y1BVMiIiIiIjeAUCjETTfdREdHBxMTExw7dozR0dF6N2tZUzAlIiIiInIDCAaDbN68mVtuuYWzZ88yODioYOoTUjAlIsuOy+VyfoXDYSKRCMYY0uk0mUyGWq1GrVbDsqxrvrYxBmMMlmXN6/0iIiJLidvtJhaL0dDQQGNjI8lkkmg0SigUwuNRKPBJ6W9QRJadQCBANBolEAiwZ88e7r33XtxuN6+88gqvv/46hUKBXC5HsVic0/XsAArA4/Hg8XiwLItyuUylUlnMH0VERGRRRSIRHnroIe6//378fr9z/yyXywQCgXo3b9lTMCUiy47X6yUWixGJRLj11lt54okn8Hg8TE1NcejQIVwuF+Vyec7BFHwUUHk8Hnw+H5ZlUavVFEyJiMiyZk88/vqv/zoAw8PDTE1NMTo6it/vr3Prlj8FUyKy7ESjUdatW0djYyNtbW34fD6MMbhc8zvtoampiba2Nvx+P16vF4/HQ6VSoa+vj/PnzyvdT+QyPB4PiUSCaDSKy+XC4/HgcrnIZDIMDw+Ty+Xq3USRJSkSidDW1kY4HHYesyyLsbExhoaGFnwSz74/ut1ujDH4/X6CwSB+v9/JypD5UzAlIsuKMYbNmzfzzW9+k87OTtrb2wkGgxQKhXldz+12s2fPHr7+9a/T1NTk7MWamJjgRz/6Ec8884xWp0QuIxwOc++997Jnzx4ndSgYDHLs2DF+/OMfc/z48Xo3UWRJ2rhxI7/5m7/Jxo0bAZxsimeffZaf/OQnTE5OLujnzdwD7PF4iMfjhMNhmpqalOa3ABRMiciyk0wmue2229i4caMzq1YoFJybxbUUjzDG0NnZyWc/+1lWrVrlPD40NMQbb7wx79UukZXO5/Oxfv167rjjDkKhEMlkkkgkQiQS4fnnn69380SWrObmZu666y52797t3MOKxSK9vb2LnnZnjCEQCBAIBAiFQrjd7kX9vBuBgikRWZZmFo0wxuB2u2lra2P79u2kUilOnjxJsVh0KvtdiWVZDA4Osm/fPvr6+li1atWsoEpELq9cLtPf38+BAwdIJBIEAgEaGxvxer2z+qfSZEVmS6fTHD9+HLfbTWNjI+3t7cDi9ZVSqcSJEyd4+eWXicVirFu3jpaWFhoaGrj11lup1WqMj49z6tQpMpnMorRhJVMwJSLLmj1o8/v93HLLLYRCIUZGRvjpT3/K6OgolUqFcrl8xYCqVqvx/vvvk06nicfjPProozz++OOzBoMicqlsNssbb7zBhx9+yIYNG2hra2PLli0EAgE8Hg9utxvLsqhWq/VuqsiScu7cOf7qr/6Kt956i9tuu42HH36YaDR61Ym/TyKTyfCzn/2MvXv3smbNGr797W/zwAMPsGrVKv7O3/k7fOUrX+Htt9/mu9/9LidOnFiUNqxkCqZEZFmyU/nsgMflcpFMJrEsyzlLwy5xfrU9T5ZlMTo6Sj6fJxqNcvvtt2vwJzIH5XKZ8+fPMzw8DEwHV263e9ZGdxG5VDab5cyZM4yPj9Pa2kqhUCASiSza+Yblcpne3l56e3sZHx9nZGQEy7IIhUJs2bIFYwxjY2OEQqEF/+wbgYIpEVl2+vr6eP755+no6GDjxo1s3boVn89HIBAgFothWZaTiz4xMcHhw4c5d+5cvZstsuLYg79UKsWbb75JoVDg+PHjjIyMzPvgbJGVrlQqkUqlKBaLHD16lGeffZaGhgYOHjx4TUd6zIc9wVgsFpmcnOTEiROMjo5y4MABUqnUon72SqVgSkSWFcuyOHz4MAMDA0QiEb72ta+xZs0a/H4/kUiEYDBIU1MTDQ0N3H///Zw6dYrvfve7CqZEFoGdxjc0NMRTTz3FT3/6U/L5PKOjo1rhFbmCfD7PwMAALpeLoaEh3nvvPdxuNxMTE2Sz2UX9bMuyKBaLZDIZTp48yR//8R+zf/9+stkso6Oji/rZK5WCKRFZdtLpNOl0Gp/Px+DgIPl8nlKpBOAcvJtIJIhEImSzWYLB4JyuOzNf3S5q4fV6qdVqVKtVzbKLXMTuE8ViURMWInNUq9Wc4zzy+Tzj4+PAtVWi/SSfXSqVKBQKTE5O0tfXp31Sn5CCKRFZtmq1Gh988AE/+MEPaGhocB63LItyuUy1WuX8+fP09fVd9TqVSoV8Po/X66VcLmNZFn6/n0996lM8+uijTqrgx11H5EakSQaR+ZvZf65HX0qn07z55ptMTk5y7tw5BgcHF/0zVzoFUyKybFUqFfbt28eHH36Iy+WateHd3q9RqVQ+Nm2iXC5TqVRwu93OClcwGOSuu+6iq6uL/v5+crkc/f39GjiKzKD+IPLJXc9+lEqleO6553jppZeoVCoqhb4AFEwtAcYYZyBopxW5XK5ZqUWVSuWqFclEblS5XI5cLuf8eb5n21iW5QRgtVoNYwyRSISWlhZyudyiH6QoshTMvB/ZfQHmP9ibeb0rnfnm9Xqdypv2irKILI5qtcrU1FS9m7GiKJhaAgKBAMlkklAoxOrVq9m5cycNDQ2kUilGRkbI5/N0d3dz4sQJyuVyvZsrsqTZ5dKvVpbZHihePECcuTHXGIPP56O5uZmpqSkCgcBiN12k7oLBIMlkkkAgQC6XI5VKOZN587n/hMNhmpub8fv9pFIpxsbGZk0MBgIBtm3bxsaNG8lms3zwwQf09fVpxUtuGBcfcK3v/vKjYGoJCAaDrF69msbGRnbv3s1XvvIVOjo66O3t5fjx46RSKYwxnD59WsGUyBxcKaC6XIB1cb56qVQinU7jdruJRCKEw2EmJia0MiU3hFAoxKpVq4jFYoyNjVGtVsnn8xSLRSqVyjUP9CKRCGvWrCEcDtPf3+8EZza/38/O/5+9Ow+u+7oS/P69b993POwACXADJZKgSEkUSVGWumVbtCxLchTbSbem25O2K+nqTvVUKpOkZiapqcwkPUmnpxy7Uy2n3Va7bbfjsdy2W7IkazFtU6ZIkZS4geIGgNiXt+/rL3+Av58AEtwgklh4PlUskcDDw33Qu/j9XNWKqQAAIABJREFUzr3nnrNpE4899hjT09MkEgmGhoYASSEUd4/Ld29v1Xv/8muezKnbQ4KpRWI2m3G5XNhsNoLBIA0NDYRCIcLhMH6/H5/Ph9/vJxgMYjKZiEQiRKNRcrkcuVyOQqGw2C9BiCXLZDLh9XpxOp1z0oxmX1hmp/Xl83kymQyappHL5ZicnKRSqWCxWPB4PEb6rd1up1arLeimUojlwGazGdeiarXK+Pj4x2q+a7fbCYfD+Hw+UqkUZrN5zueVUjidToLBIPV6nYaGBpqamiiXy6TTaeMMoxBLhd1ux+fzYbFYKBQKZDKZm05Ntdls+Hw+41iH3uR6vlTYWq1m3PfNvm5djcvlwuv1zrnu6Sm0lUqFer1OoVD4WIvzeuaGXu22VCrd1em5EkwtEp/Px4MPPsjq1asJh8P09PQQCoVobGwkEAhgMpkIh8OYTCaKxSJWq5VVq1aRSCT41a9+xeHDh+/qN64Q1+LxeHjkkUfYunUrNpsNj8czZ2dJKUWtVjMCowMHDvDqq6+Sy+V47733KJfLhMNhnnzySR555BGsVisNDQ10dXWRz+eZnJycc05LiJUiGo3y+OOPs3btWg4cOMDIyMjHOqDe2trKpz/9aVpaWvjFL37Bhx9+OGfuWCwWGhsbWb9+PaVSCafTycMPP8zFixd55ZVXOHfu3K14WULcMuvXr+epp56isbGRd999l5dffplEInFTz7F69WqefPJJOjs7UUphtVqNoAfm7iBlMhn27dtnNPTNZDJGWfXLmc1ment7+eQnP0kgEMBsNmOxWKhUKly8eJHx8XFSqRTHjh1jcHBwwT8Du93OmjVraGtrI5vNcubMGSYnJxf8fMudBFOLxOPx0NvbywMPPDAnmNJX0QFjh6perxMKhdiwYQOTk5OMjIxw9OhRCaaEuAqn08l9993Hs88+i9PpJBKJ4Ha75+Sl1+t1KpUK5XIZpRS/+tWvSKVSnD59moGBARobG+np6WH37t2YzWZCoRAtLS0kk0lSqZQEU2JFikQiPPjgg2zbto1cLscrr7zysZ4vGo2yc+dO1qxZw/Dw8BVnD/W51dHRgcViob29nWKxyNGjRzly5IgEU2JJUUrR0dHBM888Q09PDzabjX379t10MNXS0sKTTz7Jtm3bjIBHv/e7fNdpYmKCTCZjVJUtlUqUSqV5d6dMJhPr1q3jueeeo7m52cioKBQKHD16lNOnTzM2Nsbo6OjHCqZsNhudnZ1s2rSJqakpxsfHmZqaumszNiSYuk0cDgcul2vONmu9Xje2avWP6RPIbDZjNpvJZrNMT09TLpeNj+mrFQ6HA6fTidVqXeyXJ8SSpq/0ORwOHA6HkY4wm16xDyAcDrN27Vr8fr/x+XA4bKQJ6jtTnZ2dOJ1OaU4qVjT9muT3+1m1ahUmk4nJyUmGh4dvuqqsvkBosVhwOBwEAgHy+TzFYtFYXdcfo39fq9WK2Ww2bi6FWGwmkwm73W4EPqVSiVwud9Wg5kZYLBasVivVapV8Pj9nV0opZVy/nE4n0WiUrq4ukskkuVzuimp8Xq/XKGTW3NyMzWYzMpv0dFmTyUQoFKJSqdDQ0EBjYyNms/m695V6WnutVjMWE/Xq0xaLBbfbTXt7u5H2ODk5edcdRZFg6jYwmUy0tbVxzz334HA4jAmTz+d5//33OXv2rPFYffLoZdD7+vp46aWXGB0dxe124/P5cDqd3H///TzwwAPU63XjBk8IMT/9QqSn911+TkN/jNlsxm63s2PHDuMmLxaLMT09jdPpZN26dZhMJnw+Hw8//DAbNmygr6+PkZERxsbGFuGVCXH76delnp4evvrVr5JMJnnttdf44Q9/SCqVuunn0894RKNR7rvvPlpbW7lw4QIDAwO3eORC3B5Op5Pm5mY8Hg9Op5OzZ8+SSqXo7++nVCp9rOeOx+NcuHCBfD5vzBWLxUJ3dzddXV243W4ee+wxenp66O/v52//9m+vuP709PTwxS9+kY6ODhobG7Hb7eRyOU6fPs2pU6cwmUysX7+e++67j0QiQaFQoKmpCb/fz4YNG4hEIgBXnCuefaY4l8vxxhtvsG/fPgBjkaS1tZWtW7fi8Xg4efIk3/ve9zhz5szH+pksNxJM3QZKKWOl2+v1YrPZsNvtpFIpLl68OCcvVqe/aUdHR3n77bc5c+YMfr+faDSKz+ejpaUFh8NBrVbDZrMt0isTYnnQd33tdruxQqev9s1+jL5z3NXVRVdXF9VqlYsXLzIwMIDJZKKxsdEIzNatW8fatWuxWq1zdrCEWIk0TaOlpYXm5mZKpRLDw8P89Kc/XdDz6H+8Xi+dnZ24XC7jeijEcmC1WgmFQoRCIex2OxMTE+RyuStK/d8ofSdWKUU6nWZwcJBUKmUUoNCLk3V2dmK32+np6WHjxo2cOHGCl19++Yrna2lp4Xd/93fp6emhUCgYWVBDQ0McPnwYu91Od3c3nZ2dhEIhxsfHMZvNRKNRdu/eTUdHB/BRmfbZ8zaZTJJIJIjH45w7d47f/OY3xuNMJhPBYNBI5Q2FQrz66qsf++e93EgwdQvZbDYcDgd2u51oNEp7ezterxer1WqkHOnnNsrlMiMjI5w+fZrm5maamppwOp14PB7uvfde/H6/sYVqt9uJx+McOnSIXC7H2NjYvI0PhRAfmV31SF+80C9amUwGmFkxN5lMRKNR2tra5qQi6WkduvkCMiFWqvkW/T4ur9dLR0eHcRbY4XDg9Xppbm42UpIuXLjA2NgYH374oTQWFUuGw+GgubmZlpYWotEozc3NuFwufD7fvJkP1zP7zK7NZiMcDuNyueYEUx6PB5PJRKVSYXJy0ghm9N1hfXHC5/OxYcMG4/5ST5XVq2N2d3djtVoJBAJG2nooFKK1tZVgMIjD4UApRSaTYXx8nGKxSCAQoLGxcc79q9frZc2aNezYsQNN07BarQwPDwMYaX36a2lqaqJYLC6o0uFyJMHULeTz+Whra8Pj8bB9+3Y+8YlP4Pf7jdWH8fFxfv3rX6OUIpVK8ctf/pIjR45wzz330NjYiMPhoL29nT/6oz8ygq1z586Rz+fp6+vjF7/4BblcjqGhobvizSnEQun53ZVKBZPJhNVqRdM0+vv7+eY3v8mJEyeMx5pMJvbu3cvzzz9PJBIhHA4bFyWn02mc29BvLvUVRSHEjdGDss7OTvx+P9VqlWw2SzabNYpOmM1m4vE4P/7xj3nzzTfJZDIf64C8ELdSIBBg165dbN68GZ/PZwQaAwMDC8oWqlQq5HI5UqkULpeL3t5e43P6op1efj2ZTPL222+zf/9+YrEYFy5cAGbm01e/+lU2bdpEOBwmGo0aJcv19ju9vb3GuceGhgZMJhMOh4MNGzbQ0dGBzWbD7/ejlGJwcJAf//jHDA8P8+CDD/K5z32OSCRitPHx+/0888wz7Ny5k6mpKX7605/y1ltvsX79enbu3Immafh8PjZt2oTNZmNkZIRTp059rGqgy4UEU7eQvi0bCARobm42LhyzeTwelFIUi0UjxcHhcJBIJCgWi3i9XlatWoXZbKavr49isUgsFuPYsWPs37//quUwhRBz6ecQZ+9OJZNJ3n//ffbv3288zmKxsHbtWqOqn37gdz7zNQIWYqW5XnPrmzF7Z9jn8xEMBq/62EKhwJkzZzhw4MC8/XaEWCwul4u2tjbWrl2Ly+UiFAphMpmMLKKbpTeIL5fLeDwegsHgVYtAVCoVBgcHOXz4MNlslmQyCcwEeL29vezevdt4zssX/BobG2lsbAQ+agxssViMlMXZUqkUfX19nDlzhsbGRqPHm8ViMbI2vF4v69atM1oXDA0N4fF4jAIaNpuNhoYGcrmc0dbnbiDB1C0UCoXYvHkz0WiUjo4OzGYzlUqF8+fP09/fz8TEBBcvXrziApFMJjl8+DCpVAqPx0MkEsFms1GtVolGo/j9fhKJBJVKhXQ6zcWLFxkbG7trS1AKcT3FYpFTp07x5ptvEgqF2LJlC21tbYTDYXbt2jXnImI2m9m0aRMOh0PS+MRdL51Oc/LkSTRNM9oBwPwB1o2YmpriwIEDjI+P09raytq1a+ekz9brddLpNNlslvHxceOmTK5vYinJ5/P09/fjdrtpbGzE5XLhcrkWvMAWi8U4ePAgU1NTOJ1O43x9e3v7vHMkn8+TTqfnNNuNx+O88847JBIJowT67PLqZrOZSCRCQ0ODkaqnl0kfHBxkamrKWMAPBAKEw2G2bdtGU1MT69evv6KNwWx6Gr1+P6vPWbfbTVdXFx6Ph1KpxJEjR276Z7McSTB1iyilWL16NZ/73OdYvXo1Xq8Xp9NJPp/nF7/4Bd/97ndJpVJMTk5ekaI3MjLCD37wA2NCNTU14XK52LVrF5/5zGeMPPPt27czPT3NSy+9xOTk5IIOPQpxN8hkMrz22mscPHiQtWvX8id/8id0dHSwatUqvvKVr8zpEaWUIhAI4PV6F3HEQiwNY2Nj/PznP+fIkSPcd9997N2792PNjbNnz/Ltb38bv9/Ppz/9aZqbm+fcKFYqFYaHhxkYGGB4eJhYLGbsJguxVOiBS39/P1u2bKG1tRWXy7Xg5xsaGuJ73/venIrPDoeDz3/+87S0tBhzRNM0qtUqyWSS0dFRKpWKEUwNDg7ywgsv4Ha78Xq9RCKROV9ns9nYuXMnu3btwu12Ew6HsdlspFIpXn/9dQ4cOEB3dzdf+MIXCAQCdHZ28oUvfIFSqYTX68Xn8133degBlT5ng8Egu3btMsb45ptvMjExseCf03IhwdQt5Ha7aWlpob293VitqNVqTExMcPLkyavmjRYKBSPlz+fzkU6ncblcbNy4EZfLRTAYpF6vY7fb8fv9hEIhLBbLFYfrhRAzqtUq4+PjTExMGGcUa7UaTqeTrq4u43Gzm/jO13l+vo/rqYNCrESFQoHR0VEKhQLt7e1G+queOqQXYrnR6042m2VgYACHw8HWrVuNRcDZjbMzmQyxWIxkMkm5XJZrmlhyyuUy09PT1Ot12trajGBhdh+1G0lN1e8NC4WCUbxhdh+2iYkJY8FdT1WvVquUy2WKxeKcRfRcLsf58+dRSuH3+2lqajJ2k0wmEzabjTVr1lAoFDCbzUa/qFKpxPj4OGfPnsVqtZLNZqnVatjt9jk70fPtuOmvcfZ1cPa9qN1uN84c6/eqd4O741XeIfl8nrGxMSOvdCHlkyuVCqlUimKxyPvvv88PfvADozxmZ2cnZrOZPXv24HK5SCQSvPfee9KrQ4h56DdkyWSSt956i0Qigdfrpb29HY/HYxwinr1KPvtr8/k8x48f58MPP5yTytDf3y89psSKVSwWGR8fJ5vNEovFqNVqmEwm2tvb2b17N9PT0wwMDNxwISS9j00gEKC9vd1oUnry5EmOHz9uFFWamJggmUwyOTl5B16lEDenXC4zNTVFLpdjzZo1VCoVlFKsXbuW5557jsnJSeM9PV/fKf08bmNjo3H9aW9vx+VyGc2qzWYz27dvx+FwUC6XOX78OCdOnGBqamrOdehy+vmrVCpFoVAgEonQ0tKCx+OhubnZqNiXzWZJpVJGn8Tp6WnOnTvHyy+/zKlTp+aMtaWlhTVr1uByuXC73Xg8HqrVKsePH6evr4/JyUnOnz9vjEkPvu7WNHkJpm6hdDrNhQsXKJfLtLe343a7AW4qZaFYLBqT9O233+bQoUN4vV7+8A//kE2bNtHc3Ew0GuWJJ57gwoUL/MVf/AWDg4OykifEZfSVsomJCf7hH/6Bn/70p7S3t/PYY48ZKX9er9cIpi7vrZHNZnnllVf4/ve/T6VSMeZYuVw2DgALsdLk83mGhoYwm81s3ryZSqWC2Wxm7dq1PPvss8RiMV577TXGx8dv6NoWiUS4//77aW5uZt26ddhsNsrlMvv27eOFF14gnU5TLpepVqvUarU5KbhCLBX6TpLe/LZcLmMymejt7aWrq4tMJsO3v/1tzp07d0UwpRd+8Hq99PT00NraSldXF48++igtLS1GAKKUwuPx4HK5yOVyvPXWW3zrW98inU4bPaiuNb5yuYzFYqGxsdHo+bR69WqampoAOHfuHBcvXmRkZMRYFJyamuL8+fNzFhWVUuzcuZPnnnuOxsZGowx8oVDg7bff5u///u/JZDJMT0/LveclEkzdQuVymXQ6TTKZJBKJGG8yu91u9A+oVCrXPOuk58fCTGUVvShFJpPBbDZjt9txOByEw2GKxeLHytkVYiXQ+7EppahWq1fMr0qlwvT0NDBzUZuamjLyx+dbWdd7f2QyGSYnJxkaGjKqGgmx0tXrdUqlkpGKlM/nKRQKRpUuq9VKMBjE7XZjsVium/Ln8/kIhUJGywGTyWQUnRgeHjZ65gixlOm7PzCz4JDP58nlcthsNmP3p6GhAbfbPWfxDT5q0OvxeIxCD3pvw7a2NqONhx4sFQoFstksU1NTDA0Nkcvlrjs+PVXQYrHgdDrx+/0EAgGjrHmtVqNQKJBIJEgmk+RyOUqlEqVSac7z62mL8XjcWNyv1WpGWm4ymWR4eJhcLmekOgoJpm4ZTdMYHR3ll7/8JaFQiEcffZTu7m7sdjs7duygVqsRj8c5cOAAJ0+epFarXffshT459J4B+veBhVdWEmIl0VcJt2zZYrQTOH36tLFocfn8ymQy9PX1GWep7r///jmfr1QqvPfee7z77rtGSwLp6SbuRpqmcebMGb7zne8QjUZZtWoV69atMzIjVq1aZZzBuNZ1qaWlhXvuuQefz0ckEplzdkNWtcVy1N/fz/e+9z1aWlrYtGkTu3btwul0sm3bNr7yla8YDWx1+rzweDx0dHQQCASM/k3VapXTp08bVflgZu4VCgXee++9Gw5Yuru72bNnDw0NDbS1tRkpenoz+lKpxJkzZ3jjjTdIJpPzFoWw2+14PB7sdjtNTU20tbXR0tJiFKLQ71v1XeT5zhffrXNagqlbaHh4mMnJSWPn6JOf/CRer5eHH36YBx54gLGxMaNUun7I9lrBlNlsxmazYbfbMZvNxhtVAikhZpjNZjZu3MiXvvQlbDYbP/rRjxgaGjLKK18+v9LpNMePH8dmsxEIBIyUIj3Fr1wu89vf/pZvfOMbJJNJisWiBFPirtXX18fg4CBOp5Pnn3+e3bt3Ew6H6erq4oknnriiOMt81yb9OqanOs1OpxViOTp//jzf/va3sdvtfPGLX2TTpk34fD4efPBBtmzZctX39uyzUfpcqFarnDp1im9+85v09/fPCUqKxeINZUUopVi3bh1f/vKXWb9+vVEdUP9+FouFcrnMqVOnePXVV6/6vDabjUgkYhRT6+joMApSwJXB1OzrqwRT4pbRU4yq1SqpVIqpqSlMJpOx5VosFgmHw0QiESqVCqVS6ZopfzabDZvNZuTQzr7hq9frFAoFudETdz39omSxWPB4PIRCIVwul9EQUdM04xe/1WrF5XIZZWj1nhyFQoFcLmdUFYvH42QymUV+ZUIsrnK5TKVSoVgsGulBVqvVuCHUK4bpjTmvt9Cnp0al02lyudxde+Mllje956fFYiEWizE9PX3Nnkw6PZ0PPqrUV6vVmJ6eJh6Pk0gksNvtOJ1OzGYzTqcTm8025zmUUsbc0/+tlCISiRAKhQgGg5TLZUql0pz5lclkqNfrxvPZbDbj8/pCh9/vJxwO4/F48Pv9RlZUPp8nm80Sj8elD9xVSDB1G9RqNQ4dOsTXv/51I+Vvz549eDweHn/8cSNH9vLI/vILkb6qYLPZ2LRpEzabjUqlwsDAACMjIwwPDzM1NXWnX54QS0a9XmdoaIhf//rXeL1eWlpa+OM//mPgo1W0UqnExMQE6XQaj8dDa2srbreb7u5uwuEw9Xqd999/n9dff53p6WmOHDkybzUmIe5W1WqV9957j6997WtGY3n9pmvz5s2sWbPGuCHTXb5bVa/XOXLkCG+88QaTk5McPXpU5plY1mq1Gh988AHf+MY3FtSLTS/gcubMGeLxOCaTiW3btvH444/jdDqNhb3ZJdf1ioCX94Dq7u4mFApRrVY5e/Yshw8fNs5C6d8nGo3yla98hWq1SjqdJp/PY7VajYbBXq+XaDSK0+lk1apVRgW/gwcP8sYbbxCPx43Uw8uDKf1c2N2aOSXB1G1Qq9WM8pGBQACfz8eOHTtwu93s3r2bhx566KafU3+j5vN5Ll68yAcffMD4+DiJREJWCMRdq16vMzo6ysGDBwkGgzzzzDM89dRTOBwOY15kMhlOnz7N+Pg4oVCInp4eQqGQkf5Qq9U4deoU3/nOdxgZGZm3iIUQdyt9Z/fYsWOcPHkSq9VKd3c3q1evpqGhgUAgwKpVq+akLl1+jmp2OtOLL77I2NiYkS4kxHKkv7/7+vo4e/bsTQcRs+/b6vU61WoVq9XKpk2b+IM/+AOCwSAXLlxgYGBgzhlDr9fLhg0bjAp9Or1wRK1Wo7+/n9dee41YLDbn65544gmefvppqtUqY2NjJJNJnE4njY2NuN1uXC4XoVDISMs1m80UCgU++OADXnzxRWKxmJHidzl9nus7ZncbCaZuE31VPJ/PMzk5ycDAwFUr7+mpfHqqhE5PPdIP+um9b0ZGRpicnCSRSFAsFu/EyxFiySqXy2SzWcxmM9PT04yOjuJ2uwkEAni9XqrVKj6fj2KxiM/nw+VyGT03JiYmjP5wuVxO5pMQ89CrzOo3UtlslmQyidlsZmRkhAsXLhgVNWd/jX7G12QyGY20C4WC7EiJFUFfaLiVxy3y+Tzj4+OUSiXy+byR0qfvLulNcOv1ulF1Vk9n1+fp6OgoiUSCVCo1Z1Fj9rzTi5vpf+x2O3a73fh3NpslkUgYVW31eWu1Wo1z/HqxCr1flv49stks5XLZCL7uBhJM3WbFYpE333yTkZERoyLf5VpbW9m5cyfNzc3AzJu+Xq9z6tQpDh48OOfsRrVaZWpqygik5qvIIsTdQtM00uk0g4OD2Gw2CoUCBw8epKGhgWeeeYY9e/Zgt9tpa2sjHA4bbQpgZkXxhz/8IcPDw5w/f15KNAtxA2q1GlNTUxQKBRwOB7FYjNdff33eFenZBZM0TeP8+fOk0+k7PWQhloVqtcqBAwfIZrN4vV7Wr1/PunXrjABKKUW5XGZ4eJhYLMbIyAi/+c1vGB4eNp6jXq8zNjZGf3//nOApEAgwMjLC6OgoJpOJYrE4b6Nd/e/Hjh3jpZdeYnx8nDNnzpDP540eVnqa4Y4dO9i4caOR8WE2mxkfH+eNN95gaGiIvr6+u6YnowRTt1mlUuHYsWMcP378qo9Zv349Pp/POJiolKJWq3Hw4EF+9KMfEY/Hr/gaSe0TYobe8wNgdHSUAwcO0NbWxubNm3n44YexWq2Ew+Ervm54eJhXXnnF6Pwuc0qI69MXMPSgqL+//6a/XghxpXq9zpkzZzh79ixut5vPfe5zrF69es5CfKVSMe4JT506xcsvv0xfX98VzzV7nimlqFQqxhksq9V6zbRETdMYGBjglVdeYWBgwNh9s1qtBAIBWltbaWpq4nd+53f4xCc+MeesVCKR4MCBAxw/fpxEInFDPbJWAgmm7pCrXUCUUhSLRUZHR403uL4zNTU1Ne9BPyHE/PRUh1KpxMDAAIcPH56zqqc/BmbK20pFMSE+Hpk/Qtw6s9P1YrEYFy5cwG63z/vY4eFho7re9VSrVaanp42UXJi5JtrtdmKxGC6XC5fLRTgcxmq1UiwWWbt2LcFg0CiAYbFY6OjooLW1lVAohM/nM+5hx8bGSKfTfPjhh8RiMbLZLMVi8Zrtf1YSda3/CUop+S15G+m5sC6Xi8bGRlwul9GoF2BycpLR0dEb6jMgbpymaUuu3IzMtVvLbrfT3d1Na2urcWbj8mBqfHycs2fP3jUrZ4thqc01mWdiJZJ5tvKYzWYaGxtpaGi4anGXbDbLyMjIda9hSimsVitNTU1EIhHjeWCmuJnD4TDOQEWjURwOBx0dHWzYsAGXyzXn/KPT6cThcGC322lpaSEcDjM4OMh3v/tdDh8+TCKR4MKFC6RSKaMF0EoJqK41z2RnapHMrnxSKBQ4f/48MPPG1neopKqYEAtTKpU4deqUkcKnV8O8vGP7SvklL4QQYuWo1WqMjY0xNjZ2xdkm/bp1M/2eqtUqIyMjjIyMGF+r05/b4/HQ1NSE2+0mHA6zffv2OU179Wum/v319MNsNsuxY8d46623qFarlEqlu64HqgRTd5DFYiEcDhvN0PRO03qVFsBoPqqUMqr5lctlxsfHjXKyQoibM1/gJOlJQiwtNpuNhoYGvF4vDocDn8+Hw+Fgenqas2fPSiNtcVfRd4Pmu1bd7PXr8sfP9+9qtUoul6NarXL+/Hl+9atfzTlvPF8wZTKZGBgYYGxszKj2eTdeWyXN7w7yeDw89NBD3HPPPQSDQbZs2UJbWxswt8GhvvoQj8cZHx8nm83y+uuv8+qrr1IoFBZt/CvFUkuJAJlrYmVaanNN5tnSFgwG2bNnj1Eh7J577iEajbJ//37+6q/+ig8//HCxh7gkyTy7O1wtsLpVzGYzVqsVk8mE3+8nGo1e0bLn8t0wfeF/bGzMqIi7UjM+JM1viTCbzQSDQdrb22loaOCee+6hu7sbuLJbPMycmQqFQqRSKY4ePXrXNkMTQgix8lmtViKRCO3t7TQ2NrJp0yba2tqYnJzE6XQu9vCEWFS3e8dnds8svd/V5VX/bia18G4iwdQdZDab8fl8RKNR/H4/1WrVqMGvvzltNhtOp9M4FBgMBjGbzUZDNCGEEGIlKpVKXLx4EavVyurVq9m4ceNiD0mIu9r10gPFDAmm7iCz2UwkEmHVqlXYbDbjLNRsXq8Xq9WKzWbD7XZjt9vxeDz4fD7ZmRJCCLFi5fN5jh8/Tn9/P1u2bGHnzp2yEi7EIpF5d+MkmLqD9PNQegWUYrFIsViUjW/tAAAgAElEQVSc8xiTyYTX6zXexPJmFkIIcTeo1Wpks1nK5TLxeJxCoUCxWKRcLsu1UAixZEkwdQfl83kOHz5MsVic09F6NqfTaexO6RePUqnEoUOHpN+UEEKIFUvTNCqVCvV6nZGREX7yk59w4sQJ+vr6mJ6eXuzhCSHEvKSa3x2kNzy7Wjdr/TGzG4zCzAUmn89TKBRkde4WWGqVj0DmmliZltpck3m29OnXPovFgtvtxmq1Ui6XjZLN4koyz4S4/a41zySYEnedpXbhAZlrYmVaanNN5plYiWSeCXH7XWueSUUDIYQQQgghhFgACaaEEEIIIYQQYgEkmBJCCCGEEEKIBZBgSgghhBBCCCEWQIIpIYQQQgghhFgACaaEEEIIIYQQYgEkmBJCCCGEEEKIBZBgSgghhBBCCCEWQIIpIYQQQgghhFgApWnSqFoIIYQQQgghbpbsTAkhhBBCCCHEAkgwJYQQQgghhBALIMGUEEIIIYQQQiyABFNCCCGEEEIIsQASTAkhhBBCCCHEAkgwJYQQQgghhBALIMGUEEIIIYQQQiyABFNCCCGEmJdSakAptX2xxyGEEEuVBFM3SCn1p0qpf66UOrjYYxFCCCHuNKXUv1BK/bPFHocQQiwlStO0xR7DsqCU8gJ1wKtp2vhij0cIIYS43ZRSA8B/pmnae/p1UNO03CIPSwghlgzZmbpx/wVwHPi/lFKtN/vFSqm9SqnXlFLnlFJjSqkzSqlXlFLhWz9UIYQQ4pZ7FPiZUur4x3kSpdSDSqn/+xaNSQghFpUEUzdAKWUF/k/gvwUOAweUUo1Kqc8ppR66xtd9WSn1T5f+mQf+CvgJ4AYOAK8DxRscQ7dS6vuX8tffUUp99tLH/Uqp/0Eppa7xtRcl510IIcRCKaUeBv4aeAF45mM+3VHg33zsQQlxl1BK2ZVSP1ZK/eVij0VcSYKpG9MA2DRN+5mmaX8BvAM8B/w7wHaNr3sASAJomvZLoBH4FNCradrzmqb9x2ulSyiltiql1iulHrn0PU8CnwS+A3zj0sMeBz6lXSVfUynVcen7pm70xQqxUimlnEqpbyml+pVSX1NKORZ7TEIsEw8C7wL/BKxRSv0vSql7FvJEmqaVNU1LXLrG/UwpNaiUevtai5NC3M00TSsxkyH1WaXUTqXUP1NK9Vzt8Uqpf6uU+vqdG+HdTYKpG5MBTEqpPUqp+4DtwARwGviXSqkdSqngpd2q7ZcKVfwT8DTw72HmJg7435nJPb9w+TdQSv2+Uuprl334s8DLwN8DzwJ/A3QzsyrYf+kxZ4BtSqk/Ukp1KKUCSqmuS2mF/ytwEHhB07Szt/QnIsTy9F8BG5mZWw8B//XiDkeIZeM7wGpmrod/Dngv/X0OpdQL1ypSoZTapZRqV0p9Afg58CrwGDMLhn9+OwYuxEqgaVoB+DEz169vMJPxdDX3c2kxX9x+lsUewHKgaVpGKfVvgP8ElJlJdfhPzKTp/c/Ad4F2oAqMACcufe73NU1LXHqaTUBS07TTV/k2zwMvXvaxacAHeIDfADlg4NL3/stLYzt26aL03wH/x6XHJoCzzKQk/ueapv3qY7x8IVYEpdS/Bv41M/PsJDDKzOKEEOL69gJB4NOapr12jcdNA1sAlFL/I1DTNO0/zPr8P2dml8sO/A4zWRMPMJN1ceo2jFuIZU0pFWHmeEgrMwvr/56Zxfx/eyntbwBwMrPY0Qt8iZmMpD9cjPHejaSa3x2ilOoGjgCNmqZdcU5KKXUa+MrswEcp9R+ZSS/8b+7cSIVYeZRSLmZW0X8NdAAtzNz0PaVp2pHFHJsQS5lezY+Z875fut7inFLqb4Fzmqb9O6XUWeAPNU37zazP/wfgv2Qmfd4KpIFzwPeAr19KZxJCXKKU+gTw0qV/fhf4F8zMn3/LzNGRJqAEXASOAW8A39M07Vo7V+IWkmDqFlJKvQ18TtO09FU+vw+YAv6lpmnnlVJ2YA8zK+R/ycyO1n9/6eF7mVlBf0LTtAM3MYYngF2apv2rhb8SIVYWpdQqZlJjrZqmVZVSQSCtaVptUQcmxDKhlEoDWzVNO3+Nx2xkJrV856WsiSKwTtO0i7Me84/AO5ftVgkhxLIlZ6ZurS8wTw75LM8xs3rwvlKqwEy+69cAxUwQ9WmgwEw63/8G/N7NBFKXvM1Mup8Q4jKaplUv/TchgZQQN+VnwItKqccunQ/uUko9r5R6RCnVqZT6n4D9wJ9rmnbs0tdcBD6vlDIppRxKqS8DjwD/3yK9BiFWJKWUZFgsItmZWgRKKRMQAoqapmUv+5yTmdXzeXe3hBA3T9+Z0jTtqi0EhBBXdylV9l8xU1ipg0sLg8xkV6SBV4D/R9O0d2Z9zePA/8tMGpKJmXO8f6Zp2m/v7OiFWNmUUk2apo0v9jjuVhJMCSGEEGJBlFKeyxcF53mMF6hfqxWIEEIsVxJMCSGEEEIIIcQCXLM0ulJKIi2x4izFVC+Za2IlWmpzTeaZWIlknglx+11rnkkBCiGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBJJgSQgghhBBCiAWQYEoIIYQQQgghFkCCKSGEEEIIIYRYAAmmhBBCCCGEEGIBLIs9gJtlMplQSmG1WnE4HJjN5nkfV6/XKZVKlMtlNE2jXq+jadodHq0QQgghhBBipVpWwZTVasXj8WCz2Vi7di27d+8mFArN+9h8Ps+hQ4c4efIk5XKZZDJJPp+/wyMWQgghhBBCrFTLLpjy+Xw4nU62bt3K888/z6pVq+Z9bCwWw2azkUwmyWazFItFCaaEEEIIIYQQt8yyCqY0TaNWq1GtVqnX65jNZsxmM9VqlXK5TL1ep1wuUyqViMfjZDIZKpUKlUqFer2+2MMXQgghhBBCrCDLKpiqVCqkUilyuRxTU1OMj4/jcDiYmJhgaGiIfD7PwMAAg4ODZLNZzp07x9DQEJVKhUKhsNjDF0IIIYQQQqwgyyqYqlarZLNZTCYTqVSKZDJJPB5ncHCQ48ePk0wmOXLkCEeOHDF2qmRHSgghhBBCCHE7LKtgSqdpGqlUigsXLlAoFLh48SJjY2Nks1kymQzVapVarSbV+4QQQgghhBC3jbpWwKGUWrLRSCQSobOzE6fTSTabJZvNUq1WSaVSpFIpNE2TYErMS9M0tdhjuNxSnmtCLNRSm2syz8RKJPNMiNvvWvNs2QZT81Hqo9cpgZS4mqV24YHlN9eEuBFLba7JPBMrkcwzIW6/a82zZZnmdzUSQAkhhBBCCCHuFNNiD0AIIYQQQgghliMJpoQQQohlbnaauxBCiDtnRaX5CSGEEHeTcDjMpk2baGhoYGpqirNnz5LJZIwG9pL+LsTdQeoGLJ4VHUzJG0sIIcRK1t7ezpe//GUeeOABDh48yN/93d8xMDBAOp0mHo9TrVZv+jnl2inE8qKUMv7MnrMyf++MFR1MwUcXBbPZbLzJ6vW6lE4XQgix7DkcDlpaWujq6mJkZIRAIIDb7aZUKi0o9c9kMmGxWFBKUa/XqVard/RaaTKZjJtCnaZp1Gq1OzYGIZYjfd6YTCZMpplTPLVabUELKuLmrPhgCiAYDLJ9+3ZWr17NxMQE7777LmNjY4s9LCGEEOJjSSaTHDp0iEKhQDqdZseOHWzevJmjR4/y1ltvkUqlbur5Wltb2bFjB9FolHPnzvHuu+8avRtvN5fLxZo1a2hqasJsNuNyubBarYyNjXHs2DESicRtH4MQy5lSip6eHrZt24bNZuP999/n6NGjlMvlxR7airaigyk9So9EIjz77LN86lOf4siRI4yNjUkwJYQQYtmLxWK8/fbb9PX1sXHjRp544gmi0Sg/+MEPOHjw4E0HU6tXr+bLX/4ymzdv5qc//Snnzp0jnU4Dtz9lyOPx8MADD7Bt2zacTifhcBi3283BgwcZGRmRYEqI6zCbzWzdupU//dM/xev18sILL3Dq1CkJpm6zFRtMKaWwWq1YrVbcbjfhcJimpiZCoRA2m01S/K5BKYXZbMZut2M2m6lWq5TLZUmPFEKIJaZSqZBMJjGbzRSLRbxeL5FIBI/HY6T63Ayr1Yrf7yccDuPxeDCbzbdh1PNTSmGxWLDb7TgcDjweDx6PB6fTeUfHIcRSZTKZsNvtWK1WqtUqpVLpihRYl8tFNBrF5/Ph9/ux2+0UCgXq9Tr1en2RRr6yrdhgyuFwsG7dOjo7O2lvb6exsfGKPGwxl8lkwmq1YjabaW9vZ8+ePbS0tHDhwgUOHTpEMpmkUCiQyWRkQgohxBJQLBYZHR0lmUzS0dHxsVegc7kcQ0NDeL1exsfHjYqAd2IRLZfLceTIESYmJvD7/XR2dhIIBOjr6yObzd727y/EUhcOh3nsscfYsGEDFy9e5I033mBoaMiYo/qfer2OyWSioaGBDRs2kEgkmJiYIBaLyYL4bbAigymlFHa7nQ0bNvDQQw8RDoeJRqPGwdbFGhMs7coqJpMJm82GzWajq6uLL33pS/T29rJv3z6SySSDg4PE43Hy+bwEU0IIsQTowZTJZKKnp+djB1OFQoGhoSHsdjvj4+OUy+U7dt3K5XIcPXqUY8eOEQgE2LBhAw0NDYyNjZHL5e7IGIRYysLhME8++SRPPvkkBw4c4NSpUwwNDQHMCaZg5p4uEomwYcMGYrEYlUqFeDy+pO9Dl6sVGUzBzJvI5XIRDAbx+/3YbDZjZ8pqtWKz2ajX69Rqtdv6xlJKGakK9XqdTCZDPp+/bd9vofSfSygUwuv10tjYSCAQwOv1EgwGiUajlEol4+dYqVTIZrPkcjk0TTMqyMBMzq7JZDIqQUngJYQQt4+evqOnYn8clUqFRCLB5OQk6XT6jlbR0zSNSqVCpVIhn8+TyWSw2Wzk83mp5icEH93ber1ePB4PgUCAQCBApVKhVCoBGDtT9XrdOHuolMLpdC7y6FeuFRtMWSwW2tra2LRpE263m0AgYLyZotEo7e3t5HI54vH4bT2YZ7fb2b17N4888giFQoHXXnuNgwcPLqkAQw8yo9EozzzzDFu2bKGpqYnW1lbMZjPd3d0899xzZDIZstks6XSaXC7HL3/5S/bt20e5XMbpdOJwOLBYLHg8Hmw2G8VikcnJySUZPAohhLjSxMQEb775Jj6fj/Hx8UVLrysWiwwPDzM9PU2hUDBuFIW42+mL1w0NDezatYtoNMrQ0BAffPAB+XyeSqVCOp3G5XLR2dnJE088wfT0NFNTU5w8eXJJ3X+uFCsymNI0DYvFQkNDA2vWrMFmsxl9M2w2G4FAgMbGRuLxOOl0+rYGUzabjd7eXr74xS+SSqWM80dLiR5MBYNBHn30Ufbu3WtMVqUUra2tNDc3o2ka5XLZKMEbj8fZv38/1WoVh8OB2+3Gbrcbh5+TySSpVEqCKSGEWCZisRiJRGJOX8bFUC6XmZycNP4tqUlCzG3O6/f76e3tpampiaNHj3Lu3DkKhQLVapVCoUCxWKSpqYk1a9YwMTHB22+/vaCiNOL6ln0wpZQiEAgQDoexWD56OY2NjQSDQSPlTH/z2e12WlpaSCaTWK1WxsfHb/nN/uwx6YGb0+mkWq3S2tpKT0/PVZuoVSoVisWika6h/zefz5PP52/6gmK323G5XFgsFhwOBy6Xi1qtRjweJ5FIYLFYiEajBAIBVq9ejd/vx2KxUCgUiMViFItFPB4P4XAYq9VqjEmv9KdX/mtoaKClpQWbzUYwGMTtduN0OhkeHr4VP1IhhBA3SCmF1+ulvb0dpRSZTIZUKnXDgdFSWbmWAEqIq7Narfh8PsrlMl6vF7PZPOfM1OwjGPqGgrg9lnUwpZdRffDBB/n85z9vpPIBOJ1O1q9fj91un1N4oqmpiSeffJLdu3fzzjvvMDQ0RDKZvOHvp7vWL3mLxcKOHTt4+umnCYfDrFu3Dp/Ph9Pp5POf/zz333//nDe7/l9N05ienqa/v59cLke1WjXyYPv6+hbUK6CpqYl7770Xn89Hd3c369evp1qt8o//+I/8/Oc/x+fz8fTTT/PII48QDAZZt24dAIODg/z4xz/mwoULbNu2jaefftpopGi327Hb7Ubw6vF4eOyxx/jUpz6FzWYzynaeOHGCoaEhxsfHb2rMQgghFka/edqwYQO/93u/x/T0NO+88w779u2jUCgs6Pl0SzG4WQ7FnYS41TRNw+12s27dOjo6OpiensZqtV6xEH95UQpxeyz7YEo/07N3716am5uNj1+Nz+ejt7cXgEwmg8vluuHvdaNMJhNdXV1G80SLxYLZbMbhcLB9+3a2b98OzA2k9Df+0NAQR48eJZlMUi6XKRaLFItFEokEH3744Q2PQR+z3+9nzZo1RCIR7r//fnbt2kWxWOTUqVO8/vrruFwu7rvvPp5++mljp0kP6vbv38+RI0dQSvH4448br00PYvXtYrvdzsaNG/nkJz+J1WqdsxPo9/tvasxCCCE+HqUUzc3NPPTQQ6TTacbGxti/f/9NP8f1PraUbtAkqBJ3C/09brfbaWpqAqChoQGLxXLV1FwJqG6vZRtMzc4bnf1v/Q1TqVSYnJwkHo9js9loamrC7/ffVFCkF7HQd2R0uVyOwcHBq3Zjr9frjI+Pc/jw4TmphrPHabfbaWhowOfzGU3YTCYTDoeDcDiM3W6nUqlQLpcpl8v09PRQKpXI5/MkEgnS6bRRUe9aB3NdLhctLS1z0h6tVisdHR3s2LGDQCBglI0vl8vE43FyuRyZTIZVq1ahlKKrqwu73Q58NIlNJhNer5empiajWuHs/yezq/sJIYS4/er1OuVymVKphFIKt9tNvV7H4XDcdIqPpmlG5Vuz2UwoFDIWB3XlcpmhoSEmJiZu9Uu5Ifr4AKMKoBArkd4DVD9iAXMXEMLhMFu3biUSibBu3TrjDPvsx0qa3+2zbIMpYM5N++w3Vb1eJ51O8+abb7J//34ikQjPPPMMW7duxWQy3fBNvtvt5oknnuCZZ54xfmEDnD17lr/5m7/h0KFD864CVKtVfvvb3zIyMmLs4MwOMsxmM01NTezdu5d7770Xt9tNNBrF7XYTCoW49957jZLi+p/e3l6SySTZbJYDBw7wwQcfkE6n+fDDDxkdHb3qz6e5uZmdO3fS0dGB1+s10vP27t3Lli1bsFqtrFq1CpPJRDqdZv/+/Zw7dw6/38/evXvx+XzG2a96vW68BovFQmdnJw899BBOp5Pm5uZF7eMlhBB3O32BLZ1OYzabaWxsxOfz4ff7F7S4pS/uud1u9uzZw969e/F6vcbnp6amePHFF/n5z39+x0uX68FiIBAAMAoeCbHSmM1mvF6vcY+oL27rlFLcc889/Nmf/RmFQoGGhgba2tqw2Ww3dc8rFm5FBFOz6TtT5XKZwcFBDh8+THt7O4888sic3NEbuem3Wq2sXbuWhx9+GIfDYXzc5/Px0ksvzdkVm719qmkao6Oj8wY5ZrMZs9nMqlWruPfee2lvbze+RimFw+GY870ul8lkSKfTTE1N4XA4uHjx4jVfg9frpbW1lY6ODuNjehpiV1fXnMeWSiWGh4c5ffo0GzZsoLu7m+7u7nl/ViaTCZ/PR2trK3a7HbfbPefnIYQQ4s6avTPlcrlwu91YLBbsdvuCfi/rrS48Hg9dXV3s3LmTUChkfH54eJjXX3990X7nW61W49qTz+evuBYLsRLolahdLhcOh2NOppQuEokQiUSu+Lik990ZyzqYmq9Job5T5XA4WLduHXv27CEcDtPQ0DBnhwhmCid0d3dTrVZJpVJMTU1dUWVvvgDM7/ezY8cOIxVPr+s/Hz2FT0/j08fQ2NjImjVrCIfDRrW9G2GxWGhpaWHz5s0kk0ksFgutra2YTCYjHQM+apzb29t7w+fCnE4nXV1dKKVoa2vD7XZfdUXDZDIRCoXo6uoyKsrcbLAqxO1itVppbm4mFAphs9mMAjCzzT6zmEqlSCQSRof4eDy+ZCqaCXGjpqeneffddxkbG6Orq4ve3t4rrnvX43Q6CQaDOBwOWlpa6Onpwe/3Ew6HGRoaIpFIEAwGCQaDt2TMJpPJqP5qs9mIRCL4fD7S6TQDAwMkk0lMJpNxHle/drpcLmPXTSlFMpkknU4bzeJrtRqZTIZz584xPT19S8YqxO2klKKlpYX29nbjvlFv7ePz+XC73TQ3N9PQ0IBSing8zqlTp0gkElitVhwOxxX3bMVikVKpRDwep7+/X65rt8myDaZmd3ieHXmbTCY0TcPn8/Hoo4+ybds2rFYr4XD4ijS0hoYGdu7cSWdnJ319fRw4cIBMJnPF96nVasbz62/2559/nmeffZZsNsvExMRVqySZzWbC4fAV3392CXE9ELoRdrudTZs2sXr1aorFIvfffz/T09NG/yx91UIPrAKBwA1f9Px+P7t27WLbtm3Y7fZrpoaYzWY6OztxuVyYTCYikYjx/0Ryc8ViczqdbNu2je3bt+Pz+Vi/fj2NjY1zHjP7d8jZs2d5//33SSaTvP/++7e9/5wQt8P58+d58cUX8Xg8PPXUU6xZswafzzcnJf56K9V+v5/NmzcTiUS49957efTRRwkEAoyMjBgFibZs2YLP57slYzabzUSjUWPx48EHH2TNmjWcO3eO73//+8aiod4MfteuXfz+7/8+zc3NWCwWrFYrMJNerwdR2WyWYrHIhQsX+Na3viXBlFgWzGYzmzdv5tlnnyUYDBIKhQiFQkZGk35mKhwOAzAwMMA3v/lNjhw5gs/nIxqNYrPZjPuver3O9PQ08XicUqnE1NTUHU/HvVss22AKPto1qtfr1Gq1OW8SpZQRxMy+uZ+9a+J0OolGo2iaxvj4+JydHcCobgcYQQLMrHp3dnYCM2l3fr//qr2q9Lx1vcjD1S5ketB2I3w+Hz6fj1KphMlkIhAIGAUtXC6XEUzpgdDs5768YIf+2jRNw2QyXRF4XT6m2WfUXC4XkUjESE+8/PUIsVj0hYTW1lZCoRAbNmygra1tzmNm/+7QNM1InfX7/VitVmq12hU730IsZdlslkKhYPRQnJ0xoWdGXO86oy/0NTQ00NraSldXF8FgkEQiQSwWo1arGQWQZi80LpR+/fB6vQQCAdrb21m7di3lchmPx2MUTbLb7TgcDqLRKD09PUaK/OVV/PQdqVwuB8wEh7P778h8FkuVXgG5s7OThoYGotGokVV1udk7rydOnMDv9xOPx417MZPJRL1eZ2JigomJCdmRus2WdTAFM2+o06dP88Mf/nDeMtyapuF0OmlvbycYDOJyuYhGo3g8HrxeL2vWrCEajRpRvf4LGGbOG/X09BjFGT744AMGBgbweDysXr3aWB1wOp1XpBDN/v6ZTIZYLEa5XGZkZITp6ekF/0LX05ei0Sj1et2o5qc31rVYLOTzeUZGRiiVSkxPTzM8PEylUjGa9trtdrq6uujs7KRQKHDs2DHOnz9/3TGZTCbWrVvH5s2bcTqdWCwW43XrlWaEWArK5TLnz583KnK2t7dfEUzpTCYT4XCYnp4ecrkcXq+XVatWkclkOHr0KGfOnJELkVg2Zu+4wtyU7FgsZjRsv9rve5/PR09PD93d3XR0dGCxWKhUKkxMTHDixAny+TzJZJIzZ86QTqdvan5cfs5YH2sikZjzuVAoRHd3N5/5zGfo6enBZrPhdDqxWq1s3boVt9sNYJwP028s9cIbXq8Xl8tFU1MTjz/+OK2trUxNTXHixAlisZhRKVfmtVhK6vU6/f39vP7663i9Xrxer7GzPJ+zZ88yOTkJzMyFVCplnB2Emd8FC+ktJ27eigimDh06xJkzZ664mdcvFuFwmEcffZSenh4aGxvZvn07brebYDBIb28v9XqdBx54gGKxOOeXq8lkwuVyoZQiFovx0ksv8bOf/Yy2tjY++9nPsnHjRsLhMGvXrr1qykOlUuHEiROcOnWKeDzOr3/9a44dO7bgX+Iul4vdu3ezdetWo0GuxWKhWq3S3t6O1Wolm81y4sQJpqenOX78OPv27SOVShGJRIhGowQCAZ566ikaGhqM1/WTn/zkumOyWCx84QtfoKOjw8jjtVqtV5SpF2KxFQoFjh49ahRTuf/++9myZcsVj9Pfs83NzUQiEer1Og899BClUonJyUm+/vWvc/78ebnpEsuCnoWglDJ2oPRzRps3byYej3Py5EmSyeRVg6lwOMyDDz5Ib2+vUXq8VCoxODjIb37zG2KxGPv27cNut1Or1UilUjcVTF2+k1StVpmamiIejwMz15mmpiYaGhro6uoyyrzrX+d2u43rbaFQIJlMUiwWGRwcZHh4GKfTSW9vr/H1zz//PMVikaNHj/LXf/3XnDx50ggIZV6LpaRWq3H8+HH6+/sxmUzG2fer3VsVi0WSyaTx96mpqSseK9kVd8ayD6ZgJrUhm81e9fPFYpHJyUkjnzSTyRj9nfRgQE+Pg49yr/V+VYVCgUwmw8TEhFE9T//lb7VaKZVKVy1AUS6XyWazxGIxYrEYo6OjXLx4ccG/xD0eD+Pj48RiMex2u7HTZLfbyefzxlj1lIyJiQmGhoaMJsD6RTafz1Ov16lUKsRisRsak9VqZXJykmw2i8fjuW7JzWKxKPm5YlHocxfmvg/nq/6pV0q6PFXVarUa/dOEWE5m91usVqtYrVaCweAN9ZzSCzjp5xdzIlYAACAASURBVGH1XSS92FImk7lqY9CrPZ+eMm82m41iS/quEnzUI6pYLBrzUb8uX/66Zqenl8tlMpnM/9/enQfHdV15nv++3JGJxJKZWAmA+yqSIkWJ2poS5bKsllWyVB63l2rbM92ast2uqKmZipmOmO7oqZia6Z6eiIqYinHXHt0ul1VyeyuXPPIqWZtLomguIMV9EQBi3xKZidzXN3+A7zkBghQJggRB/D4RDFHAQ+ZNMF++d+499xw7qIpGo/j9ftLpNIVCwd6z7HQ6GR0dpaGhgdraWiqVispFyx0pk8lcddvItZimaRdQq57ork5Xr16xlsV1VwRTHyabzXLmzBmmpqaor6/n7Nmz1NfXU1tbSyQSwefz0dHRwYYNG3C5XBw7dowjR47YN2GVSoWpqSnOnz8PwPT0NIcPH2ZoaIhgMMi777571Yp5pVKJwcFBhoaGSKfTjI2N3dQsQaFQoKenh1KpZO+NcrlcBAIBDh8+TDAYJBaLcenSJZLJJMPDw/YFyiqTHgqF7GaOVs779dwwWrMmf/M3f3NdwZQ1UyhyO1nv9UcffZSdO3fS2tpqV6mcy9rHON/3FETJcmXtAezu7qa5uRnDMHjwwQeJx+OMj49z+vTpq85Yx2Ixjh07RjabpaWlhXXr1uHxeGhqamLLli2Ew2EmJibsdPXq60D1zZr133A4zPr16wkGg0QiEVatWgXAoUOHOHjwIPl8/orWIte6JpXLZUqlEsVikffff5+33nqLZDJppy+63W4uXbpEa2srzc3NPPLII6xbt87u7bhr1y5OnDjBz372MyYmJhb19y6y1JxOJ9u3b+fRRx+lpqaG/v5++vr6yGQyjI2N3dQ2E7m6FRFMpdNpTp48yZkzZ2aVWLU2sjY0NLB3714ikQg1NTW88847/NVf/ZWdvmBF9Llczi6jfPDgQXu2zeVyXfWD30q7qF7putlg6sKFC/T29gK/vuGrXhK2SsNawVKxWLT7QnV2dhIKhQgGg/a4rnemolKpcPz4cc6dOzdvWt/c/y+VSguaYRFZKOt9WVdXx1NPPcXnPvc5vF7vvHsar6cvmgIqWY5M02RsbIzDhw/bVfkeffRRUqkU3d3ddmq4dWy1eDzOkSNHGB4e5t5776Wjo4OamhpaW1vZvn07k5OTnD59mng8Trlcxu1243K57Jlxa4LO+tPc3MxDDz1Ea2srW7ZsYc+ePZimyV/+5V9y/Phxu2pmdauCaxWKKJfLFAoFMpkM3d3dvPjii0xNTdkTn06nk3fffRev18vWrVtpa2tj7dq1tLW18fzzz1Mul3n55Zd57733FEzJXcVa/d29ezdf/epXCYVC/PKXv+Stt96y231Eo1EFU7fAigimrCa+1QzDwO122x/Ck5OTTE5O4vP57D4ziURi3jedFVgtFSsl4kZZaX1WasTk5CSxWOyGXks+n7dTM0TuJIZhEAgE7CqToVDIrk6ZzWaZnp6+oceLRqNks1ldeGRZyuVydmGHdDpNsVi8rv0T1ROA1fuufD6f3bDXahBaLpft7IjqSbzqrIdgMIhhGLOeu7oarNfrtb8XCoXwer3X/RqttMHq1iJWqxGXyzUrTdHpdFJbW4tpmrPS+kXuNtb5V30+3khqrty4FRFMXU0qlaK3txefz2c3CHS5XJw4ccLe9Ap3R5nvSqXC8PAwhw4dwufzce7cObt64ZkzZ3SSybLn9XrZt28fjz32GKFQiJ07d2IYBuPj4/z0pz/l5MmTN/R4mUyGw4cPa9+fLDvWytShQ4fw+/32fqJisUhPT89V9xDCTHXatrY2urq6aGpqsjM5Vq9ezRNPPEE2m2Xv3r32DPfcFiLVezMqlQojIyN2H7ezZ89y8uRJampq8Pl8/O7v/q49XtM0aWxsZNu2bddMu7VKpQcCAR555BF8Pp89IWgdY2WLRCKRWSm+Ko8udzNrdbi7u5uvfe1r1NTUMDAwQH9/P7lcTil+t9CKDqasgg0APT09HDhwYNaGvbuJaZqMj48zOTl5RQNHKwVRZDnzer3s3buXF154gUAgYG90j0aj/OQnP+GVV165ocezzo277bNAVgarWafL5SKZTNoV/AYGBma9p+cGLlYvp46ODrthqGEYdHR00NraCvChAUl1MPWjH/2In//855w/f55wOMz7779PfX09n/70p/nMZz5jlzm3xjJf2nz1/1up+m63mz179rBr166rHmulPc2l653crSqVCqdOneLcuXP2/1fvY9R7/9ZYscFUdX42YKcoOBwO6uvr7cpH8Xj8qul+y40qucjdzuVy2c09q89xq1KYyEpR3aw9nU7bJZRhppGtaZq4XK4rgo3Gxka76Xu5XLYnHDOZDOl0+rquIdbqlGmaRKNRcrmcfQ6mUil7z9Z8VTTnUyqV7NYluVyOdDo9byP6q41l7v+Pj48vKFVeZDkol8vKqLjNVmwwdTVut5t9+/bxm7/5m1QqFV5++WVee+01ffCKLBPWyuvdMAEicrMqlQoTExMUCgU8Hg+RSIQHH3zQ/rtVmdXS0NBgN6RPJBKcO3cOh8PBiRMnOHTo0A0VFTJNk8HBQbvQQyaTYWJiglwux/T0tL3y+2F9ChOJBB988AHT09N88MEHHD16lGQyucDfCAwMDKj4hIgsGgVTczidTrZu3conP/lJyuUy586d44033lAwJbJMVM9UK6CSlc6qQJtIJPD7/XR2drJx40Zqa2vp6uoiEolcNa3O6itVKBQ4ePAgP/jBD0gkEgsaA8xUoy0UCrN6HVo9n64VTGUyGQYGBhgfH+fQoUO88sorTE5O3vA45huTiMjNUjA1j+r9RHJ9nE4nTqcTr9dLc3MzwWCQTCbD8PDwNRsqi4jI7VEul0kmk0xMTNify9da4alurzE5OXnTrT0s1n7ETCaD2+3G5/Ph8XjI5/MMDw8Ti8Xw+/2sWrWKYDBINptlZGSEoaEhe5VN12cRuVMomJqHKv7cGIfDQSAQsKtAPffcc+zevZuzZ8/yzW9+k1OnTi31EEVEVjyrmt/k5CQul8sOYq7F2iM1OTm5aG0xTNNkenqaoaEhMpkMTU1NNDY2Eo1G+fa3v83bb7/Npk2b+OIXv8iuXbsYHR3lzTff5MyZMyQSCfUvFJE7ioKp66Cg6tqsnl01NTU0Njaya9cufuM3foPa2tobrqAmIiK3RqVSIRaL2f2n4PobUy9mnxrTNMnn8ySTSVwuF/X19cBMoYz333+fV199lVgsxjPPPINpmnYbkzNnztg/LyJyp1AwNUe5XKa3t5c33niDcrlMX1+fKuBdh+oeIA6Hw077ExGRO4dpmgvaT7jYAUx1Bsh8Fffi8ThHjhyhXC5z9OhRu+m2AikRudMomJqjWCzy9ttvc/78eUzTZGRkhFKptNTDuuNZ+8ysHiDVHehFltL1zryLrBTVAclCg5ObbWpf3dx37nisflhf//rXCQaDJBIJhoeHFUiJyB1JwdQclUqF0dFRRkdHl3ooy0512siNppCI3Ap6/4ksPmvyDG6uEei19ian02nOnz+/4DGKiNwuCqbkppmmSS6XwzAMRkdHeeutt4jFYly4cIGxsbGlHp6sYCokI7K43G43W7duZfPmzRQKBU6dOkVvb+8NBVWGYeD3+wmHwzQ0NFBTU3OLRy0icusomJKbVqlUSKfTZLNZUqkUL774IrW1tWSzWTVGFBG5i/h8Pvbv388Xv/hFkskkf/EXf8HAwIDdgPdaAVX13tr6+nra29tpaGjA6/VqFVlEli0FU7IorNz3UqlEPp+3+3SVy+WlHpqsIJVKhUKhgMfjmZVuKiKLwzAMOxCanp62V5aKxSLlctm+FpTL5VmBlZUa6HK58Hg8eL1eampq8Hq9wExD32KxqIJPIrLsKJiSRWdtLFaKldxOxWKRU6dO8fLLL9PY2Mi2bdtYu3btUg9L5K5SLBbp7u7mpZdewul00traygsvvECpVGJ6eppCocD4+DjHjh2zMxOsQGrDhg3ce++9NDY2snPnTrxeL/l8nhMnTnDhwgWGh4fp6+vTdUNElhUFU7LoNLMoS6FQKHD06FGi0SjNzc389m//NqtXr1ZQL7KI8vk8Bw4c4MyZM7S2tvIv/sW/4JlnnqFUKjE2Nsb09DQnTpxgZGRkVpq3w+Fg27Zt/PN//s9pbW2lra0Nn89HIpHgH//xH/n7v/97UqmUqvaJyLKjYEpE7gqmaZJOp5mcnMTlcpHL5XRTJrLIrPOsXC7jdrspFou4XC4Mw8Dr9eLz+fD7/dTV1dHQ0GCn27pcLkKhEE1NTYTDYZxOJ6lUikQiQSwWY3Jykmw2Sy6XW+qXKCJyQxRMichdoVKpkEqlME0Tp9NJNpu1e59p75TI4jBNk1KpRC6XY3Jykh/96Ef09fXhcrlwu932+fb000/zzDPPAL9O89uyZQsdHR14PB5OnTrFyZMnicViHD58mFgsZu+bEhFZThRMichdwTRNUqkU6XQat9ttl+tXIQqRxVUqlexiQz//+c95/fXX8fv9dHR0EA6H2bZtG1/4whfYunXrrJ9zu9243W4ymQwXLlzgW9/6FvF4nMnJSWKxmFJyRWRZUjAlIncN62asukSz2+2msbGRVatWkc/n7U3yInJzTNOkUChQKBTsyQyPx2On7k1NTdnHVk9oWG0zkskk6XSafD6vvbYismwpmBKRu1okEuGTn/wk9957Lz09PfzoRz+it7dXs+Aii6hYLDI5OUkymWR6eppkMkkoFJr32FKpxIULFxgdHSWfz2tyQ0SWNQVTInJXq6+vZ9++fezdu5eDBw/yq1/9ir6+PgAFUyKLpFQqEY/HARgfH+fChQvXPN5aQRYRWe4UTInIXclKK7I2vzudTpxO5xKPSuTup4btIrKSOJZ6ACIii606kJqPUvxERERkMSiYEhERERERWQAFUyIiIiIiIgtgKNVFRERERETkxmllSkREREREZAEUTImIiIiIiCyAgikREREREZEFUDAlIiIiIiKyAAqmREREREREFkDBlIiIiIiIyAIomBIREREREVkABVMiIiIiIiILoGDqDmMYRp9hGPcv9ThEREREROTaFEwtAcMwnjYM4/9c6nGIiIjcboZh/K+GYXxqqcchIrIYXEs9gBXqDeDdpR6EiMwwDON90zR3LvU4RFaIPwVKSz0IEZHFoJWp28wwjC3Aj4GjhmF8+hrHrTUM4/u3b2Qiy5dhGI2GYXzLMIwThmEMX06XPWAYxiev8yE+dvlxnIZh/IlhGLW3cLgidz3DMPYahnHOMIxfGIbx8JxvdwGvGIaxcSnGJiKymBRM3X7/GpgAvgD8F8Mw/Fc5rh/48m0blcjy9ybwH4E8MAb8BOgBMAzjBcMwLhiG8d35zjnTNEcv/7cM7ACuNwgTWTEuTzb822tctzAM4+3LKXx/BHwH+H+B7xuG8RHrGNM0TwJ/Dbx4q8csInKrKZi6BS7vifrxVb79I+ATwP8HGED9VY7zAl8xDOO4YRhnDcP467kXMMMw/sAwjF7DMH5oGEbnor0AkWXGNM0YM+fW/w38O9M0HzRN849M0zxmGEYL8GfA7wAtwP8AYBjGc4Zh/GCeh3sf0Iy5yJV2A/+9aZqZ+b5pGEYdsBmIA+uAH5um+TLwfwBfrT7WNM1vARsMw9hwa4csInJrKZhaRIZhbDEM4xiwExiZ7xjTNL8PhIDPAWVgcp7D2oFfAY8zc+P3SeB+4LNVz7UD+EPgM8Aw8J8W7YWILE//Dvhb0zTnzna7mfmsczKzWrX78te3AlPzPI738rEiMls/EDYM418bhrHeMIwGwzBWG4bxpGEY/xY4BrwHvA7EgCcMw1gNPMPMajGXf67FMIyvAn5gfGleiojI4lAwtbh6gHuBs8BvGYbxW4ZhuK1vGobhv5w7/n8BLwP/yTTN4uXvRQzDiDDzb/JfgFeB/xZIMhNMrQd6Lx/7FPA2MMDMLPq5y98XWcn2AT+d+0XTNAeBfwN8E/gPQOXytwrMKcJjGIYBPMbMDaGIVDFNc5yZ/YX7gIPMTAYeY+aatoaZyb/nTdOsAP8b8C+B48xMWvybyw/zPwGXLh/7edM0p2/jSxARWXSGaZpLPYa7xuUZuDNAHTOrSP8G2ASkmZnt9jAzs3cE+IZpmj+s+tn5/iEqzMzmvcdM4PX65WMPAA1AgplZ9iLwe6Zpfv3WvDKRO59hGL8E/tw0zZeu8n0DeAv4gWma/8/l1d03gOeAA8zMkv8hMyvCD1/ePyUiInJHMgzjJ8AvTdP8D0s9lpVMwdQiMgzj60CdaZr/TdXXapgJrsrAtGmahUV4nj5m8tZfMwwjAFRM08ze7OOKLGeGYfxL4H8HXmAmzagMbAC2MzPJ8R+Z2c+x1zTN5OWf+Qzw74FOwAS+C/wvVkEKEbk6wzC2A//aNM0vLvVYRESWioKpm3S5hPIjwP/MzKb1x03T7L/Fz9kH/Hemab55K59HZLkxDOP3gf+RmeCoxMyqrYeZNKPvAX9omuYV+6QMwwgCOSvtVkQ+3OU09jrTNKNLPRYRkaWiYOomXN5w+4fAaeBvgb+2Zrxv8fP2oWBK5KouB0deZlanSrfjvBQREZGVR8HUTbhcqjx3ebOtiIiIiIisIAqmREREREREFsB1rW9epcKcyLJmmqax1GOYS+ea3I3utHNN55ncjXSeidx61zrP1GdKRERERERkARRMiYiIiIiILICCKRERERERkQVQMCUiIiIiIrIACqZEREREREQWQMGUiIiIiIjIAiiYEhERERERWQAFUyIiIiIiIgugYEpERERERGQBFEyJiIiIiIgsgIIpERERERGRBVAwJSIiIiIisgAKpkRERERERBZAwZSIiIiIiMgCKJgSERERERFZAAVTIiIiIiIiC6BgSkREREREZAEUTImIiIiIiCyAgikREREREZEFUDAlIiIiIiKyAAqmREREREREFkDBlIiIiIiIyAIomBIREREREVkABVMiIiIiIiILoGBKRERERERkARRMiYiIiIiILICCKRERERERkQVQMCUiIiIiIrIACqZEREREREQWwLXUA1hpHA4HhmFgmqb9R0RERERElh8FU7eRYRj4fD68Xi/lcplsNkuxWFzqYYmIiIiIyAIoze82cjgceDwe/H4/NTU1OJ3OpR6SiIiIiIgskFambiOXy0V7ezvt7e1kMhkuXrxIPp8HULqfiIiIiMgyo2DqNgoGg3zsYx/jYx/7GENDQ3zjG98gFotRqVSoVCoKqERERERElhGl+d1GXq+XzZs3s3//fvbu3UtLSwsOhwOHQ/8MIiIiIiLLjVamlkggEGD79u2kUilisRjnz58nHo8v9bBERGSZqq+vp62tDb/fj8fjwefzYZomg4ODXLp0iVKptNRDFBG56yiYWiLNzc389m//Ns888wzd3d386Z/+qYIpERFZsM7OTp5//nk6OjoIhUK0trZSqVT49re/zYsvvkgymVzqIYqI3HUUTC2RmpoaNm7ciGEY5PN5gsHgUg9JRD6E1SNO5E5UV1fH+vXrWb9+PS0tLaxevZpyucyBAwdwuXS5FxG5FfTpehuVSiUmJyfp6+vD7/cTDoepra0FZm7SHA7HVRv5Op1Oe29VuVymUqnc1rGLrFSGYVBbW8s999xDZ2cnU1NTnDhxgvHx8aUemsgVFOyLiNxeqnxwGxWLRQYGBjh27BhnzpyZldbncrlwuVw4nU4Mw5j1c4Zh4HK5qKmpwefzaYZR5DaxJjmampr4zGc+wx/90R/x5S9/mXXr1i310ESuSgGViMjto7vy26hSqZBOp4lGozgcDorFIjDTzNflcuF2uymXy5TLZeDXN3JWs19rM3G5XKZQKCzlSxFZEarPv/b2djZt2kQikSAQCCz10GQZq540mzt5NpeVrVD952oqlQqlUolSqUShUKBQKFAul1V4QkTkFlIwdRNudP9EPp/nwoULlMtlOjs7Wb9+PWvXrqWxsZH777+fQCDA0NAQ586dI5VK0drayurVq6mpqaG1tZXW1lYKhQIHDhzg2LFjSvUTuYUcDgfBYJBAIEAkEsHn82EYxofe/IpcS11dHdu2baOtrQ2Px0NdXR0ej2feY8vlMqOjo4yOjpLL5RgeHmZiYuKq153R0VHeeustTp8+TTAYJBwOU6lUOHTokN0gXkREFpeCqQWqvqm63oAql8vx/vvvc/bsWbZu3cpjjz0GQDgc5vHHH2fr1q0cPnyY4eFh0uk0nZ2dPPHEE4TDYe655x62b99OPB4nl8tx4sQJBVMit5DT6aShoYGWlha73LTIzWpsbOSJJ57ggQceoK6ujtWrV9t7Z+cqFAocPnyYo0ePMjU1xbvvvsvk5ORVrzlDQ0O88sor9h5bp9MJQDqdJpvN3rLXJCKykimYugFOp9O+SHk8HpxOJ8VikWw2a6fmXYtpmuRyOXK5HKlUyk69cLvdNDQ0UKlUqKursy+ADocDt9uNx+PB6/Xi8/nsPVOaHRe5PaxzLZvNMj09TTqdVtqULJjL5aKhoYGmpibq6uoIhUJXTRstFAqEw2HC4TCGYdDY2EhdXR2lUolcLnfF+7BYLJJIJK54HKfTidvtBpiVSi4iIjdPwdR18nq9dHR0EIlEqK+vZ/PmzTQ1NXHx4kVee+01hoeHF/zYPp+PVatW0djYSE9PDx6PB9M0GRoa4pe//CWBQIDjx4/T1tZGNpulu7v7Qy+Gc4MtbUiW5eRq1S2tv89dGb4V7+9yuUwikaBcLpNKpfjud79rrxxfunTpivFW//dWjUmWP7fbTSQSobOzk2w2y9mzZ8nlcldNITUMgz179lAoFFi9ejUf+chHGB8f5/XXX+f06dMf+nwOh4P29na6urowTZP+/n6Ghob0/hQRWSQKpq6T1+tlzZo1bNiwgY6ODp566ik2b97MG2+8wfvvv39TwVRNTQ3t7e2YpsmJEydwu92Ypsnw8DBjY2N2NT+n04lpmuTz+RsOpiy6gMpyYBV+gJlN9XMDKivYmu/7i6VSqRCPx5menmZkZISenh6cTiflcnnW/hPrXJs75ls1LlnePB4PkUiEjo4O+vv7OXfuHENDQzgcjis+t71eL3v27OG+++7D7XbzyCOPUKlUOHfuHAMDAzcUTN1///1UKhUKhQIjIyNanRIRWSQKpm6A0+nE6/Xi9XqpqanB7/fj9XoXpVS5ldo3t5+UdcHL5/PXvUfL5/NRX1+P2+2mUCiQy+Xsi6iqAMqdzkqDslKfqoOS+VamMpkM6XTaPl8Wcy+hVT0TuGZqn2EY+P1+6urqcLlcVCoVeyypVIpUKrVoY5Llz9rPVC6XmZ6eJhqNzgrKLR6Ph2g0SjQaxev12tecmpoaQqEQ7e3tFAoFpqenr/hs9/v9BINBvF4vra2tRCIRKpUKra2ttLe3k8/nSSaT2kslInKTFExdp+oy5aZp2vsnstkspmnaq0a3sijEhwVR1sV4w4YNPPvss3R2dtLX18eJEydIJpMMDw/T39+v/R5yR2toaODZZ5/lwQcfnLdiZnVAVS6XOXbsGAcOHCCVShGPx0kkErdtRaj6c2HHjh385m/+JuFwmHK5TLFYJJ1O84tf/IK3337bboUgYkkkEhw7dowTJ04AV2YUOJ1OTp06xU9/+lPq6+t58MEH2b59O/l8nmeeeYa9e/fS09PDD37wAy5evGj/nMPhYNeuXTzzzDNEIhGam5tpbW3FNE127NjBxMQE4+PjvPLKKxw+fFirqCIiN0HB1A2ong3P5/Ok02lyuRwwc/Fa6up61tg6Ozt57rnn2LlzJ4cPH8bpdDI6OkqpVGJoaEjBlNzRamtrefzxx/nsZz9rr9jOd6NnmiaFQoGGhgYGBweZmJigWCwyPT19W28MrYBq48aN/LN/9s9YvXq13ecnFosxNjbGu+++q2BKrpBMJjl//jzd3d1XPca67kQiEUzTpLGxkWAwyKOPPko4HObAgQMcPHhwVjBlGAabNm3is5/9LF1dXfPuMezp6eHcuXMcPXpUe/xERG6CgqnrVC6XSafTxGIxHA4HfX19FAoFhoaG7IDqRhSLRaLRKIODg3i9Xurr6+0KgX6/n0AgYN+QXc9FrnqG3GoA7Ha7CQaDtLS04HQ6iUajhEIhMpkMuVxOfUfkjuF0OgmHwzQ0NNDV1UUoFMLj8cyaqS8UCnYVTK/XSyAQwOPx0NjYyOrVqwkGg9TW1lJfX3/FxIZ1DlkNTE3TJJPJEI/HFy3IMQzDrppm3bx6PJ5ZaVuytCKRCDAzGZbJZJZ835Df76erq4upqSmSySQTExMUCgV8Ph9+v9+u6Gp9ludyOS5dukQoFKKlpcX++po1axgfHyeVStmTColEwr5OWRwOB/X19QQCAQzDoLW1lc2bN5PJZOyfFxGRG6Ng6jrl83nOnz/P8PAwPp+Pw4cP4/f7iUajC1rtSSQSvPXWW0xMTNDR0cH+/fvp6OggGAyydu1aKpUK0WiU0dFR+2J4raDK4XDg8/lwu914vV5771VnZye/8Ru/QTabpampCafTSTwep7e3l/7+/iVfTROBmZvKp556iieffJLGxkZ27NhxRcpTNBrlyJEjRKNR1qxZw+7duwkEAuzYsYNgMGjvD7RSb6tn262CELlcjqmpKXK5HKdPn+a1115jYmJi0V6HleprFY2xCsfIneGJJ54AsIs3TE9PL8k4rPfmmjVr+NKXvkQ0GuW9997jW9/6FiMjI6xatYqtW7fi9/tpamoiEolQKBTo6+vjb//2b+ns7KSxsZGuri66urp44YUX+MQnPsHhw4d56aWXGBgYoLu7mz/+4z+e1R/N7/fz2GOPsXfvXiqVCk899RQPP/wwH3zwAS+99BLHjh1bkt+HiMhypmDqOhWLRUZHR2/qMapvDrPZLOfOnSORSLBt2zb27NkDzBSPiEQi9gz8xMTEvPtGsuU88QAAHyZJREFU5rJmML1e76yZ8VAoRDgcttOfBgcHmZycJBaLMTg4qGBK7gher5ft27fziU98gpqamnmLuiSTSS5evEh/fz9Op5Nt27YRDAZZtWoVq1atmnVsdTBlFZGwikEMDQ3ZM/AHDhxYtGCq+vmsleLqgjKy9LZu3Wp/llanxS0F0zRpampi//799v+/8sorjI6O0tDQwIYNG+yV2s7OTuLxOKdOneKdd95hw4YNPP/888BM0/fHHnsM0zRxu9288sormKZJX18ffX19s56zoaHBbhRcX1/Pjh07aGpqoru7m1/84he3+TcgInJ3UDB1i1ytZ4h1s1UqlZiensbj8ZBIJOyVrWAwyLp16+wZyXA4TDabZWRkhMHBwaumpVjpgVaVwbk3cFZ6x5o1awgGg/T19eF0Ou0Ze5GlZAU8hUIBp9Npnz/pdJr+/n7i8TgDAwP09vYyPj5Ob28vZ86csScLIpGIHYDN12OtWCxSLBYpFAqzVo0skUiE9vZ2O9W2uvT6XJlMhpGREZLJpF21b27wVj0O7UW5c3R2dgIwOTlpN7G9Vaw0T6/Xa5dDb2hooLOzk0QiwalTpwgEArS2tlJTU0MkEmH37t00NTWxceNG1q5dS21tLS0tLTQ0NGAYBi0tLXR0dFBfX09/fz/vvPMO9fX1rF69mrq6Ovt9e7UJOGt1dnp6GofDQbFYVAN4EZGbpGDqFrFWioArZsitXlFDQ0NMTExQX19vl6ft6uriueees/c05XI5UqkU3/nOd/jOd75DOp2e9/k8Hg+tra2EQiGam5vx+XzA7B4469evJxQKMTU1xcjIiL3puVQqLfneAVnZTNO0b/KsPU0+n4/+/n6+/vWvc+jQIbLZLNFolHw+T19fH5cuXaKuro5HH32Uj370owSDwSua5xqGQalUIplM2uXTDcOY1dLA6XSye/duPvWpTxGJRKipqaGmpuaqN5m9vb38/d//PWfOnCGTyTA1NUWpVLInJiqVyrwNh2XpffSjH7Uns1599dVb+lwOh4NwOExTUxOhUIinn36a+++/n2QyycmTJ/nVr35lV15ds2YN99xzD3/wB39ANpu19/65XC68Xi8+n4/p6WkeeOABSqUSuVyOV199lZdffplt27bxpS99iV27dl1RdXbuRFm5XCYajdLX10c4HLZXdBVQiYgsnIKpW8RK8TEMY1YDT+viVi6XSSaTwMz+KWtfVF1dHXV1dbMeK5VKcfjw4Wv2s3I6nQQCAerr66mtrbWf22IYBo2NjTQ2NtLQ0EA4HMblcs3bKFLkdrNWj/L5PC6Xyw5OpqenOX78OG+99das4wuFApVKhbq6OtasWTPvnkVrdt6q+lfdT8dKv7Pe/y0tLezZs4f29nZqa2upra296nkRDod57733GBgYuCJwqu6HpUDqzrN69WoAmpub8Xg8t/S5DMOwe/61tLSwY8cOHn/8cXp7e3n33Xfp7u62V1+tlL/m5uarPp7T6aStrY01a9YwOjpKd3c3Z8+epVgsEo/HZ73frlZd1pq0SCQSeDweVZgUEVkECqYWUXt7O5s3byYQCNhpRNU3dJlMhjNnznDp0iX8fj9r166lqamJHTt20NDQYD/O3DQhl8vFxo0befrpp4nFYly6dImRkRFKpRL5fJ5SqURNTQ2rVq1i3bp1dHR04PV67b0bc3k8HjZv3sxTTz3F1NSUPSbd/MlSKRaLXLx4kTfffHNWc9Lh4WECgQDbt2/H5/MRCoXs6pctLS32eeTxeCiVSvT09NDb22uvAMNM4BWPx8lkMsCvz6+TJ0/aK71WQ26Px2NXVXM6nfYeE/h1EQu/38/27dvxer1cunSJZDKpypjLRDQaBSAQCLBv3z7Wrl3LwMAA58+fX1BV1muxPvOj0Sg+n49cLmf3JKytrSUSidhNnq+H0+mkpaWFTZs20dDQwPDwMJVKhc7OTvx+P4ZhEAgEWL16tR0wTUxMUC6XaWhosEuqb9y40X4MKzVQRJaHYDDI5s2baWtrm/X1kZERzp49q4qcS0TB1CIxDIOdO3fy1a9+1e7rMXfPxdjYGH/xF3/B0NAQkUiEZ599loceesjuZD+XFQx5PB6eeOIJduzYQTQa5fvf/z6vvvqqfaFOJpPU1dWxZ88e7rvvPkKhELW1tbMeo1pNTQ1PPvkke/bsYXBwkD/7sz+jv79fwZQsmWw2y5tvvsnx48ftFSNrn9+WLVvYunUrra2t7Ny5k1AohMvlsvc31dXV4ff7yWQyvPbaa/zd3/3drFUoqxz63NUrq9UBzEwwBINB/H4/AwMDnDlzBqfTyc6dO+1VqkKhQKlUor6+nmeffZZCocBbb73FhQsX7Jt0y9X2TMrS6u3tBWb2yP2rf/WvKJfL/MM//AN/+Zd/uejBVLlctkueW5kIpmnicrlobW1l7dq1tLW14fV6gdmTaNWfxdbfPR4PmzZtoquryy6a0trayrp16+zJuEgkwv33309rayvnzp0jmUySy+Xo6uri3nvvJRQK8dhjj/HAAw/g8/ns64SILA/Nzc18/vOf5yMf+Yj9NdM0ef311/na176mYGqJKJhaoLnpcQ6Hg8bGRjZv3sz69evnPbahoYFQKGTv2ejo6GDz5s124Qj49UZ8i/UcVgrI1NQUBw4coLa2lkqlYpdddrlcNDY20traSiAQmLW5em5AZc1wtrS0EAgEaGxs1B4PWVLlcpmJiYkrKut1dnaya9cuOjo66OjoYOvWrTQ3N8/7XrUqbp4+fdq+oFSn3V2NtYJspf7lcjmi0Shut5tMJmOnS5VKJYrFIi6Xi/b2dlwuFx988AE1NTX2zyqAurNZpdCbmppYt24dXq+Xw4cP4/V6F/Uz0HofWPtRc7mcvVrqdDrx+Xz2JMDc0vnzBVIwcx2x0sAdDgctLS1MTU0RDoftgMzr9drVW8fGxuwV22AwSHNzM+Fw2P7sd7lcduq5ihCJLA9er5fOzk62bdtmf800Tc6fP3/LU5fl6hRMLUA4HGbPnj2zyjE7HA4eeOABgsEglUqF/v5+Ll68SKVSYcOGDaxduxb49Yy1w+Gw9zhZM+ymaTI0NMSZM2eYnp6mUCiQz+dxOp3cc889bN++HY/Hw/bt28nn84yPj/Pmm2+SSCRwuVzU1tbS0NAw6/GqK4rNd6PncrnsGwtrg7+VDiWyVKyVqUAgQFdXF5s3b7Z7SU1NTdnnSXVBlkwmw/Hjx6+70bXFNE0mJiY4efIkTU1NFItFNm3aBMyUYz906JBdta9cLlNXV8e6desIhUKsWrWKZ599ll27dvHggw/aN7rW42py4s7ys5/9DIDt27fT0tJCTU0NnZ2d7N+/n7GxMfr6+ujp6bnhvoEWwzBoamqitbXVLgoUiURoampiw4YNOBwO/H4/GzZssFP9rAa6V3u8+d5DPp+PtWvX4vV6CYVCBAIBYGbP7ZYtW2hrayMSibBq1SoKhQJr1qxh/fr1BAIBmpqacDgcZLNZLly4wPDwMBcvXmRycnJBr1lEbp94PM4bb7xBPB6f9fWjR4+SSCSWaFSiYGoB2tra+PznP8/jjz9uf83abBwMBimVSpw8eZLvf//7lEolnn/+eTo6OmalcVhpReFw2A6wKpUKPT09fOtb36Kvr49kMsnk5CQ+n48XXniBDRs2EAgE+Cf/5J9w33330dPTw+DgIGfOnMHj8dDQ0EBTUxPAvGWd5wuo3G43nZ2d7Nixg6mpKYrFooIpWVJW6XKn02mn+e3du9feB5JIJPjHf/xHvvnNbzI8PGz/nFWwIp/P33AwNTw8zDvvvEM4HGbHjh3s3r2bUqnEe++9x/Hjx2etFnd0dBAKhQiFQmzcuJEvf/nLFItF/H4/dXV1dsuBuUUpZOl94xvfAOCZZ57hoYceorm5mc2bN/PpT3+aWCzGj3/8YwYHBxccTDkcDrq6unjwwQdpaGjg/vvvZ+fOnfZKlNPpJBgMsnPnTrZu3YrT6ZxVOfJ63yt+v59t27axadMmXC6XXb01HA7zwAMPUC6XKRaL9j4tj8eDx+Oxm7s7HA6SySRvv/0277zzjt18XkTubBMTE/zX//pf+eEPf2h/zTRNstnsFQGW3D4KphbA4/HQ1NREV1cXlUplVllkq5dNMpkkkUhQLpftmzsriLLS+jweD2632z6mXC6TSCQYHR1leHiYRCLB5OQkXq+XiYkJUqkUDoeDmpoa6urqSKVS1NbW4na77ceyNjNX97MqFotUKhX7glrNmv1vbGykXC7f8t4rIh/GSoP1er12ZT1rxTebzZJKpZicnGRwcJDBwcFFeU4rtQ9mClZYaV/lctkuqQ4z54t1rsLMCkFra6v9ONXnXT6fJ5PJqGLaHWR8fByYKUSRSqVIp9O4XC67nURjY6O9R646RdRK0asuPT4fl8tFfX094XCYUChEa2srq1atsitUVk9UWZ+11cVLrpZuN7coUfXzAbP2e1njc7vd+P3+Kx7b6rmWSqWIRqOMjIzYkxAicmcrFotaRb4DKZhaACtoyuVyjI6OcuzYMSYnJ+3yy+VyGZfLxUc/+lF8Ph9bt27F7XYTDAbZv38/tbW1NDc3s27dOmCmCssbb7zBwMAAFy9epLe3l6mpKXK5nP1chw4d4q/+6q8Ih8M8/PDD7Nq1C6/Xy4YNG3jooYfYsmUL9fX1s8ZpmiZnz57lnXfeIZ1Oc9999/HQQw/Z+7NgphjF9u3bqa2tpa+vj/7+fgYGBm7r71OkWl1dHfv372f79u2sWrWKrq4uAEZHR/n5z39Of38/58+ft1sL3Cwrze/48eMEg0E8Hg8+nw+32019fT379u2zj7X2PjY2Nl7xONa5WiwWOXbsGIcOHWJqaoojR44seKVDbo0PPviAF198kba2NtauXcu2bdtobGzkox/9KE1NTbNSRbPZLJcuXWJiYoJgMMiaNWtmVV+d24Kiq6uLtWvX4vP58Pv9jI6Oks1mOXHiBD09PbNWn+ZrMD3X3K8t1r686elpjhw5wuDgILlcbtELcIiIrBQKphbA6luTz+fp7e3lH/7hHzh37hyZTIZ4PI5hGHz605/mS1/6EpFIxL4xCwaDPPnkk+zbt88ujwswODjISy+9xMGDB+1eO9ZMqPVcBw4c4NixY3Z/lHvuuQev18vmzZsplUqsWrVq1gXeGufp06f567/+a8bGxnjhhRe49957rwim7r33Xu655x5OnDjB66+/flt/lyJz1dfX8/TTT/OpT30Kt9ttv1+Hh4d55ZVXOHr0qH2eLAbTNBkbGyMajdorUi6Xi1AoxO7du3n44YftlQhrZWK+FdxyuWxPqBw8eJA///M/JxqNksvltDp1h7lw4QJDQ0P4fD4+97nP8dBDD9mrSB/5yEdmpWjGYjHefvttzpw5Q1tbG48//jhr1qy5asVGqy1GuVxmbGyM4eFhxsbG+O53v8vrr79+xerTfI9zO9JDK5UKhUKBYrE4b4NfERG5PgqmFqBUKtk9PBKJhD3rbN1oWdWagsEgwWDQvhGzNh9bN4dWFSfrQlYul+00Put4q/mudbGtbsZrBWThcJj6+no75aNYLJJOp+3N+lNTU8RiMaLRKJOTk3YVwLlVpKp784gsFafTid/vp6GhYVY6VblcJpVK2RXZbvTxrCalmUzmipUiq7hEpVIhmUwyNTWFaZpMT0+TTqdnnStWs2ur+lupVMI0TdLptH18NBolFosph/0OZZW5z2QyxGIxYrGYPelV/e9rGAalUonGxkbC4bCdvtfY2DgrxdtKp7ZSAq3+f/F43P7ctT6H5wum5qPPYhGR5UHB1AKMjY3xgx/8gMOHDxMIBNixYwcPPPAA8OuAaseOHfNWabJy8au/HolEeOKJJ2ZVBwQIhUI0NTXZs+CGYeD3+3nggQfsfPjNmzfT1NREIBCwV6b6+/t54403GBkZ4fjx40xPT1MsFjl48CB/8id/QiAQIBKJEAqFZs24DwwMMDIycst+byK3k3WORSIRnnzySbZu3cqlS5fsVMH5Zv/L5TKXLl2yG2GfPn2apqamWefr6tWr+fjHP87GjRtJp9OMjY2RyWS4cOEChw8fJhaLceLEiVm9ruTOYwU+R48e5Wtf+xoNDQ10dXWxZs0ae/9UXV0d5XKZrq4umpqaCAaD9ufs1NQUFy9etPe5XrhwYVaqnFUQJZ1Ok06n+eCDD64rjU9ERJYXBVMLEI1G+dnPfobD4eDhhx/m93//99m+fTs+n4+Ghga7Eln1bPa18uQbGxt56KGH2Lhx46yNxp2dnWzcuBGfz2f/THWlM5fLxbp161i7du2sJsHDw8P8+Mc/5vTp00xPT5NMJikWi3R3d3PixAk8Hg/r1q1jzZo1szZTJxIJe4O2yJ1mITedhmHYe2H+6T/9pxw6dIj333+fwcHBK3q6wcwN8PDwMKOjo7NWhKv//sADD7Bz5042btxINptlaGiIeDzOgQMH+O53v8v4+Pi8TYLlzmL9+586dYpz587h8XjYu3cvDz30EMFgkK6uLtra2vD7/XR2dtqVV6s/L0+ePMnIyAinTp3i7bffnlWauDpYry5iISIidxcFUwtgmqa9B6JUKtkb1v1+Pz6fD6fTyfT0NLFYbNbNmlXpyaoWVf312traWRdbq9S6lUaUzWZnNRCdO57qxxscHCQej5NOp8nn8/bPWKlMVrpUPB6fFUylUindAMpdx+Fw2OdoTU2NXUAin8/PqtRnsdL94NcBnLWHyu/3EwqF7CapuVyOyclJxsfH7R5t1uqEbpyXh+oUT6uCaiaTsSutWhXx0un0rM/Z/v5+xsbGmJiYIBaLkc1mVcRBRGQFUjB1k9xut51iZ+Xal8tlDh48yA9/+EOmp6ftsunhcJjf+q3f4rHHHrPz8WGmZ8jq1auv2FBvVZHK5XJ0d3dz+PBhCoUCcPUSujDTh+DcuXP2fq65x5bLZSYmJshms7NuDqxyuSJ3i+qVAZgpbrFr1y4CgQBDQ0OcOnVq3qqAcwOhUCjE888/z/33309zczNr1qwBZipx/uQnP+HChQuMjY2RTCYVRC1TVopnJpPB5XLh9/sJBAI4nU4CgYDdy8mSSqUYGRkhl8sRj8eV1ikiskIpmLpJVrEJq/Giw+GgVCpx/vx5Xn75ZcbHx+1gatWqVezcuZN9+/bNCmKsvlXVKpUK/f399qzngQMHePnll0mn08CHz3pf6/vWxvob3cgvspxYK7bVkwl+v5+1a9fi8XhwOp1cvHjxukqs19bWsnfvXp5//vlZ/Xumpqbo7u7m2LFjqoi2zFUqFSYmJpiYmACuvwS5gmcRkZVNwdQiMQyDTCZDOp22S6QXi0U7fcRK10smk0SjUTulpLqJYzXTNBkdHaWvr89urjjfKpOIXJ1pmuRyOXp6euju7iabzVJTU0N7ezvj4+PXbFLtdrtpaWkhHA7T1dVFJBKxq71V32griLo7KUgSEZHroWCqirXJ/EZ7fFg3VmNjYxw7doypqSlOnz5tN921HqtYLHLp0iUOHz5MPB7n3Xffpbe396rPl8vl7DLO4+PjdoqfiFy/yclJvv3tb/P666+zceNGPvnJT7J+/XrK5TKvvfbaFcdbK8x1dXV8/OMf58knn6S+vp5NmzbNWoGGXwdSuvFemazPfv37i4isXAqmLrtaA8YbkUqluHTpEuPj40xOTtp9RyzlcplYLGZXCzt48KCdHjTfxXi+mzYRuTGpVIqTJ08CMxMaX/jCF1i/fj2tra14PJ4rjrc+C7xeL5s2bWL//v34fD68Xu8Vq1Ii8OuWFyIisvIomLqsvb2djRs34na76e/vp7e394pg6GqsYKj6z3yKxaJdRjcWizE9PX3de590oZaVIpPJcPLkSV577TXq6+vZsGED4XCY2tpa1q1bRyaTsZtm3+hqbTabpb+/n0gkQqlU4v7776ezs/OK1gUOh4OGhgbWrFmDx+OZFUTF43EuXrzI1NQUR44cua49V3L3iUQibNq0iWAwaDd6LhQKJBKJeZvziojI3UnBFDOlk/fs2cPv/M7v0NDQwPe+9z1efPFFpqenZ5VJno8VPF1Pqk82m+Xo0aOcP3+eUqlEPB7/0EIRCqJkpYnFYnzve9/jzTffZMuWLXzlK18hHA7T3NzM/v37Wb9+PadPn+btt9++4WAqGo3y7rvvMjIyQiQS4YUXXiAQCMw6xjrn3G43ra2t+P1+HA6HfZ739/fzn//zf+bQoUMkEgmGh4cX7bXL8rFp0yZ+93d/l02bNvHBBx9w5MgR4vE4J06c4NixYyqTLiKyQiiYYmYmOhwOs2PHDsLhMO+9955d7evDVpuqfdjqVLlcZmpqiqmpqVvxMkTuCvl8nr6+Pi5dumRXngTwer20tbXhdDqZmJi4ZvGIqykUCoyNjeF0OmloaGDjxo20trYCs/e/WBMoc/dRmqZJKpXi3LlzHDlyZJFesSxHdXV1bNmyhV27duHxeBgfH6empob+/n47PVtERO5+CqaYuXnq6enhhz/8IQ0NDeRyOT7+8Y+TyWQYGhpieHiYYrFINBq1+zBZF8vqPU2JRIKLFy8yNDTE6OioGuCKLJA1GVGpVCiVShSLRTweD+3t7dTW1jIyMkIoFKJQKJDP58nlclfdd1gtl8sxODhIKpWiXC7j9XoJhUK0tLTQ0dGBy+WyJzyKxSLxeNxull0qlezPiomJCRUfWOEmJyf51a9+RTQa5eLFi3R3dxOPxxkZGdFnv4jICqJgipkbtqNHj9LX10dtbS2f+MQn+L3f+z0cDgdvvvkmBw8eJB6Pc/LkSVKplL2nwuFw4HQ67ZuqiYkJDh06RG9vL7lcjmKxuMSvTGR5sgKUUqlkB0ter5eNGzdSqVSIRqN0dHRgmqbdhqBUKs0Knub7eyqV4tSpU7hcLi5cuMCpU6cIBAI8+OCDPPnkk/j9fk6fPs37779POp3mwoULDAwM2G0JyuUy+XyeWCxmp/5Vj1dWjqGhIXsCbmRkhA8++IBMJkM2m9Vnv4jICqJg6rJkMkkymcTv91MsFmlubsbj8dDc3Ew4HMYwDAKBAD6fD8MwcLlcOBwOPB6PvTpVKBSYnp4mFost8asRWf6stLpCoUAmk7Gb5Vply4PBIMFgkHw+j8/ns1cDrlVtzzRNOzhzOBy43W4CgQBTU1Nks1mcTifJZJJYLEYikWBsbIzBwUHK5TKlUolyuWxPpliVAK39ktbqlawMuVyOsbExkskkk5OTTE5O2vukFFyLiKwcCqbmKJVKdHd38/Wvf53a2lpqa2t55JFHyOfzbN68mWg0at9MGYbBhg0baGpqWuphi9xVTNPEMAwmJyf52c9+Rk9PD2vWrGHfvn20tbXR2dnJxz/+cWKxGOl02k7bswKpSqVCJpO5oghAKpViZGSETCZDc3Mz69evJxgMsn79ensPViQSYevWreRyOVpbW9m+fbu9KmWxVqStm+ZyucylS5c4fvw46XT6Nv2WZCnl83kmJibweDykUik7kFYgJSKysiiYmqNQKHDgwAGOHz9OJBLhK1/5Cs899xxut9uema7mcrnw+/3aPyGyyEzTZGxsjO9///t4PB4ef/xxtmzZwqpVq9i4cSOdnZ1UKhX7T7ViscjExIRdMdM6L8fGxjh69Cjj4+N0dXVx33330dDQgMvlsleaWltbCYfDmKZ5xTlvmuYV6b0wE7y99tpr9PT0KJhaIbLZLCMjIxiGcUWwLSIiK4eCqXlkMhkymQwA6XSaYrGI0+m090lZqqt7WTPhuqCKLJ5SqWRX84tGo8RiMeLxODC7ua7b7cYwDJxOJ06n014lcDqd9mOZpkm5XCYcDlMqlQiFQjQ2NtLQ0DAr4HI6nXi93quOab59UpVKhUAggN/vp6amxi6aIXcvK9gWEZGVTcHUNWQyGV5//XXi8Tgu18yvygqmqleiyuUypmly+vRpEonEko1X5G7W19fHN7/5TV577TU7zdbhcNDV1cX69evx+/20t7fT3t6Ow+EgGAza560lEAjg9XpJp9PU19fT1taGz+cDrn9VOZVKMTAwYAd51s96vV727t1LPB6nr6+Pnp6eG+6DJSIiIsuLca0bCMMwVnzOmsfjweVy2bPgc/9YwZSV5lEoFJTqd4czTfPqFQqWiM61D2etGFUHUi6Xi7179/LEE08QCoXYvXs3O3fuxOVyzbuCVN1g21rJulHj4+McPHiQgYGBWV+3zv98Ps/Bgwd58803SafTK/rz4E4713Seyd1I55nIrXet80wrUx+iUCjYs8vzBVPWfo2VfMMkcjuUy2Wy2SyAHUw5nU4SiQSTk5OUy2WGh4dpbGy8YkVqMU1OTjI+Pn5F820rVbBUKmlSRUREZIVQMHUDqme6q9P8dNMkcntUn2tWem1vby+FQgGv18t7771HKBSatbdxsWUyGSYmJuwG3nNVKhXGxsYUUImIiKwASvOTFedOS4kAnWs3q3q1+Hb4sCBJkywz7rRzTeeZ3I10noncekrzE5G7moIXERERWQq3LhdGRERERETkLqZgSkREREREZAEUTImIiIiIiCzANQtQiIiIiIiIyPy0MiUiIiIiIrIACqZEREREREQWQMGUiIiIiIjIAiiYEhERERERWQAFUyIiIiIiIgugYEpERERERGQB/n8Vn+pOI0GIPwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for data in train_ds.take(2):\n",
        "    images, labels = data[\"image\"], data[\"label\"]\n",
        "\n",
        "    _, ax = plt.subplots(4, 4, figsize=(15, 8))\n",
        "\n",
        "    for i in range(16):\n",
        "        img = images[i]\n",
        "        img = tf.image.flip_left_right(img)\n",
        "        img = tf.transpose(img, perm=[1, 0, 2])\n",
        "        img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)\n",
        "        img = img[:, :, 0]\n",
        "\n",
        "        # Gather indices where label!= padding_token.\n",
        "        label = labels[i]\n",
        "        indices = tf.gather(label, tf.where(tf.math.not_equal(label, padding_token)))\n",
        "        # Convert to string.\n",
        "        label = tf.strings.reduce_join(num_to_char(indices))\n",
        "        label = label.numpy().decode(\"utf-8\")\n",
        "\n",
        "        ax[i // 4, i % 4].imshow(img, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(label[::-1])\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1hPZfai5M3q",
        "outputId": "22f90cb9-b797-4cc3-ed8f-feb1e4b52e1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"handwriting_recognizer\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " image (InputLayer)             [(None, 64, 32, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 64, 32, 32)   320         ['image[0][0]']                  \n",
            "                                                                                                  \n",
            " pool1 (MaxPooling2D)           (None, 32, 16, 32)   0           ['Conv1[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 32, 16, 32)  128         ['pool1[0][0]']                  \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " reshape (Reshape)              (None, 32, 512)      0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " dense2 (Dense)                 (None, 32, 16)       8208        ['reshape[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 32, 16)      64          ['dense2[0][0]']                 \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirectional  (None, 32, 256)     148480      ['batch_normalization_3[0][0]']  \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " label (InputLayer)             [(None, None)]       0           []                               \n",
            "                                                                                                  \n",
            " dense3 (Dense)                 (None, 32, 42)       10794       ['bidirectional_1[0][0]']        \n",
            "                                                                                                  \n",
            " ctc_loss (CTCLayer)            (None, 32, 42)       0           ['label[0][0]',                  \n",
            "                                                                  'dense3[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 167,994\n",
            "Trainable params: 167,898\n",
            "Non-trainable params: 96\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "class CTCLayer(keras.layers.Layer):\n",
        "    def __init__(self, name=None):\n",
        "        super().__init__(name=name)\n",
        "        self.loss_fn = keras.backend.ctc_batch_cost\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "        input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "        label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "        input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        loss = self.loss_fn(y_true, y_pred, input_length, label_length)\n",
        "        self.add_loss(loss)\n",
        "\n",
        "        # At test time, just return the computed predictions.\n",
        "        return y_pred\n",
        "\n",
        "\n",
        "def build_model():\n",
        "    # Inputs to the model\n",
        "    input_img = keras.Input(shape=(image_width, image_height, 1), name=\"image\")\n",
        "    labels = keras.layers.Input(name=\"label\", shape=(None,))\n",
        "\n",
        "    # First conv block.\n",
        "    x = keras.layers.Conv2D(\n",
        "        32,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv1\",\n",
        "    )(input_img)\n",
        "    x = keras.layers.MaxPooling2D((2, 2), name=\"pool1\")(x)\n",
        "    x= keras.layers.BatchNormalization()(x)\n",
        "    new_shape = ((image_width // 2), (image_height // 2) * 32)\n",
        "    x = keras.layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)\n",
        "    x = keras.layers.Dense(16, activation=\"relu\", name=\"dense2\")(x)\n",
        "    x= keras.layers.BatchNormalization()(x)\n",
        "    x = keras.layers.Bidirectional(\n",
        "        keras.layers.LSTM(128, return_sequences=True, dropout=0.35))(x)\n",
        "    x = keras.layers.Dense(\n",
        "        len(char_to_num.get_vocabulary()) + 2, activation=\"softmax\", name=\"dense3\"\n",
        "    )(x)\n",
        "    output = CTCLayer(name=\"ctc_loss\")(labels, x)\n",
        "\n",
        "    # Define the model.\n",
        "    model = keras.models.Model(\n",
        "        inputs=[input_img, labels], outputs=output, name=\"handwriting_recognizer\"\n",
        "    )\n",
        "    # Optimizer.\n",
        "    lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate=0.0001,\n",
        "    decay_steps=10000,\n",
        "    decay_rate=0.9)\n",
        "    opt = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
        "    # Compile the model and return.\n",
        "    model.compile(optimizer=opt)\n",
        "    return model\n",
        "\n",
        "\n",
        "# Get the model.\n",
        "model = build_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "nvk3StRQ6OID"
      },
      "outputs": [],
      "source": [
        "validation_images = []\n",
        "validation_labels = []\n",
        "\n",
        "for batch in validation_ds:\n",
        "    validation_images.append(batch[\"image\"])\n",
        "    validation_labels.append(batch[\"label\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "LqXAS8Rp6Qi5"
      },
      "outputs": [],
      "source": [
        "def calculate_edit_distance(labels, predictions):\n",
        "    # Get a single batch and convert its labels to sparse tensors.\n",
        "    saprse_labels = tf.sparse.from_dense(labels)\n",
        "\n",
        "    # Make predictions and convert them to sparse tensors.\n",
        "    input_len = np.ones(predictions.shape[0]) * predictions.shape[1]\n",
        "\n",
        "    predictions_decoded = keras.backend.ctc_decode(\n",
        "        \n",
        "        predictions, input_length=input_len, greedy=False, beam_width=100,\n",
        "    )[0][0][:, :max_len]\n",
        "    sparse_predictions =tf.sparse.from_dense(predictions_decoded)\n",
        "    \n",
        "    # Compute individual edit distances and average them out.\n",
        "    edit_distances = tf.edit_distance(\n",
        "        sparse_predictions, saprse_labels, normalize=False\n",
        "    )\n",
        "    return tf.reduce_mean(edit_distances)\n",
        "\n",
        "\n",
        "class EditDistanceCallback(keras.callbacks.Callback):\n",
        "    def __init__(self, pred_model):\n",
        "        super().__init__()\n",
        "        self.prediction_model = pred_model\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        edit_distances = []\n",
        "\n",
        "        for i in range(len(validation_images)):\n",
        "            labels = validation_labels[i]\n",
        "            predictions = self.prediction_model.predict(validation_images[i])\n",
        "            edit_distances.append(calculate_edit_distance(labels, predictions).numpy())\n",
        "\n",
        "        print(\n",
        "            f\"Mean edit distance for epoch {epoch + 1}: {np.mean(edit_distances):.4f}\"\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41kEKtah6dMH",
        "outputId": "0cc327c3-c0ac-4e89-9cec-68c752c80eff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 98.1240Mean edit distance for epoch 1: 6.9439\n",
            "49/49 [==============================] - 32s 566ms/step - loss: 98.1240 - val_loss: 103.5902\n",
            "Epoch 2/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 45.9694Mean edit distance for epoch 2: 6.9439\n",
            "49/49 [==============================] - 20s 420ms/step - loss: 45.9694 - val_loss: 30.1254\n",
            "Epoch 3/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 16.7202Mean edit distance for epoch 3: 6.9688\n",
            "49/49 [==============================] - 19s 396ms/step - loss: 16.7202 - val_loss: 15.4529\n",
            "Epoch 4/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 12.0098Mean edit distance for epoch 4: 6.9715\n",
            "49/49 [==============================] - 19s 391ms/step - loss: 12.0098 - val_loss: 11.4525\n",
            "Epoch 5/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 9.7375Mean edit distance for epoch 5: 6.9945\n",
            "49/49 [==============================] - 20s 407ms/step - loss: 9.7375 - val_loss: 9.3649\n",
            "Epoch 6/20\n",
            "49/49 [==============================] - ETA: 0s - loss: 9.2249"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "epochs = 20  # To get good results this should be at least 50.\n",
        "\n",
        "model = build_model()\n",
        "prediction_model = keras.models.Model(\n",
        "    model.get_layer(name=\"image\").input, model.get_layer(name=\"dense3\").output\n",
        ")\n",
        "edit_distance_callback = EditDistanceCallback(prediction_model)\n",
        "\n",
        "stopping=tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0,\n",
        "    patience=3,\n",
        "    verbose=0,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        ")\n",
        "\n",
        "# Train the model.\n",
        "history = model.fit(\n",
        "    train_ds,\n",
        "    validation_data=validation_ds,\n",
        "    epochs=epochs,\n",
        "    callbacks=[edit_distance_callback],\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "#add early stopping mechanism"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eqMD9_uV78IQ"
      },
      "outputs": [],
      "source": [
        "# A utility function to decode the output of the network.\n",
        "def decode_batch_predictions(pred):\n",
        "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
        "    # Use greedy search. For complex tasks, you can use beam search.\n",
        "    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][\n",
        "        :, :max_len\n",
        "    ]\n",
        "    # Iterate over the results and get back the text.\n",
        "    output_text = []\n",
        "    for res in results:\n",
        "        res = tf.gather(res, tf.where(tf.math.not_equal(res, -1)))\n",
        "        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode(\"utf-8\")\n",
        "        output_text.append(res)\n",
        "    print(output_text)\n",
        "    return output_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PrrOwwWt8IEH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "outputId": "9c130fb9-5f1e-4862-b641-cd66f75cb1fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['نا', 'لط', 'ل', '.', 'حج', 'ا', 'وف', 'و', 'نا', 'ل', 'ر', 'م', 'ر', 'ا', 'نا', 'ا', 'ا', 'ل', 'بة', 'ا', 'ل', 'ي', 'نا', 'حجية', 'ا', 'ل', 'ا', 'ق', 'ر', 'ا', 'ل', 'د', '،', 'حج', 'وف', 'ن', 'ن', 'و', 'ير', 'ر', 'ا', 'ر', 'أ', 'نا', 'ي', 'فر', 'ل', 'نا', '.', 'حيج', 'ي', 'نا', '،', 'نا', 'د', 'م', 'نا', 'ي', 'نا', 'يف', 'يم', 'ية', 'ا', 'ي']\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#  Let's check results on some test samples.\n",
        "for batch in test_ds.take(1):\n",
        "    batch_images = batch[\"image\"]\n",
        "    _, ax = plt.subplots(4, 4, figsize=(20, 8))\n",
        "\n",
        "    preds = prediction_model.predict(batch_images)\n",
        "    pred_texts = decode_batch_predictions(preds)\n",
        "\n",
        "    for i in range(16):\n",
        "        img = batch_images[i]\n",
        "        img = tf.image.flip_left_right(img)\n",
        "        img = tf.transpose(img, perm=[1, 0, 2])\n",
        "        img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)\n",
        "        img = img[:, :, 0]\n",
        "\n",
        "        title = f\"Prediction: {pred_texts[i]}\"\n",
        "        print(pred_texts[i]=='')\n",
        "        ax[i // 4, i % 4].imshow(img, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(title)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['val_loss'][2:len(history.history['val_loss'])-1])\n",
        "plt.plot(history.history['loss'][2:len(history.history['loss'])-1])"
      ],
      "metadata": {
        "id": "FESxtfjtMwoE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "42cf27ec-b03a-46bf-d8f9-601ad37a00e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc4af8033d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WBy8QteZY4R2"
      },
      "outputs": [],
      "source": [
        "plt.plot(history.history['val_loss'][1:len(history.history['val_loss'])-1])\n",
        "plt.plot(history.history['loss'][1:len(history.history['loss'])-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ktVZxM7MXdOb"
      },
      "outputs": [],
      "source": [
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot(history.history['loss'])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "OCR_Model.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}